Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement

Semi-XctNet: Volumetric images reconstruction network from a single projection image via semi-supervised learning

Deep learning networks have achieved remarkable progress in various tasks of medical imaging. Most of the recent success in computer vision highly depend on large amounts of carefully annotated data, whereas labelling is arduous, time-consuming and in need of expertise. In this paper, a semi-supervised learning method, Semi-XctNet, is proposed for volumetric images reconstruction from a single X-ray image. In our framework, the effect of regularization on pixel-level prediction is enhanced by introducing a transformation consistent strategy into the model. Furthermore, a multi-stage training strategy is designed to ameliorate the generalization performance of the teacher network. An assistant module is also introduced to improve the pixel quality of pseudo-labels, thereby further improving the reconstruction accuracy of the semi-supervised model. The semi-supervised method proposed in this paper has been extensively validated on the LIDC-IDRI lung cancer detection public data set. Quantitative results show that SSIM (structural similarity measurement) and PSNR (peak signal noise ratio) are 0.8384 and 28.7344 respectively. Compared with the state-of-the-arts, Semi-XctNet exhibits excellent reconstruction performance, thus demonstrating the effectiveness of our method on the task of volumetric images reconstruction network from a single X-ray image.

RA-DENet: Reverse Attention and Distractions Elimination Network for polyp segmentation

To address the problems of polyps of different shapes, sizes, and colors, low-contrast polyps, various noise distractions, and blurred edges on colonoscopy, we propose the Reverse Attention and Distraction Elimination Network, which includes Improved Reverse Attention, Distraction Elimination, and Feature Enhancement. First, we input the images in the polyp image set, and use the five levels polyp features and the global polyp feature extracted from the Res2Net-based backbone as the input of the Improved Reverse Attention to obtain augmented representations of salient and non-salient regions to capture the different shapes of polyp and distinguish low-contrast polyps from background. Then, the augmented representations of salient and non-salient areas are fed into the Distraction Elimination to obtain the refined polyp feature without false positive and false negative distractions for eliminating noises. Finally, the extracted low-level polyp feature is used as the input of the Feature Enhancement to obtain the edge feature for supplementing missing edge information of polyp. The polyp segmentation result is output by connecting the edge feature with the refined polyp feature. The proposed method is evaluated on five polyp datasets and compared with the current polyp segmentation models. Our model improves the mDice to 0.760 on the most challenge dataset (ETIS).

BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series

Mortality prediction is crucial to evaluate the severity of illness and assist in improving the prognosis of patients. In clinical settings, one way is to analyze the multivariate time series (MTSs) of patients based on their medical data, such as heart rates and invasive mean arterial blood pressure. However, this suffers from sparse, irregularly sampled, and incomplete data issues. These issues can compromise the performance of follow-up MTS-based analytic applications. Plenty of existing methods try to deal with such irregular MTSs with missing values by capturing the temporal dependencies within a time series, yet in-depth research on modeling inter-MTS couplings remains rare and lacks model interpretability. To this end, we propose a bidirectional time and multi-feature attention coupled network (BiT-MAC) to capture the temporal dependencies (i.e., intra-time series coupling) and the hidden relationships among variables (i.e., inter-time series coupling) with a bidirectional recurrent neural network and multi-head attention, respectively. The resulting intra- and inter-time series coupling representations are then fused to estimate the missing values for a more robust MTS-based prediction. We evaluate BiT-MAC by applying it to the missing-data corrupted mortality prediction on two real-world clinical datasets, i.e., PhysioNet'2012 and COVID-19. Extensive experiments demonstrate the superiority of BiT-MAC over cutting-edge models, verifying the great value of the deep and hidden relations captured by MTSs. The interpretability of features is further demonstrated through a case study.

Classification regularized dimensionality reduction improves ultrasound thyroid nodule diagnostic accuracy and inter-observer consistency

PROBLEM

Convolutional Neural Networks (CNNs) for medical image analysis usually only output a probability value, providing no further information about the original image or inter-relationships between different images. Dimensionality Reduction Techniques (DRTs) are used for visualization of high dimensional medical image data, but they are not intended for discriminative classification analysis.

AIM

We develop an interactive phenotype distribution field visualization system for medical images to accurately reflect the pathological characteristics of lesions and their similarity to assist radiologists in diagnosis and medical research.

METHODS

We propose a novel method, Classification Regularized Uniform Manifold Approximation and Projection (UMAP) referred as CReUMAP, combining the advantages of CNN and DRT, to project the extracted feature vector fused with the malignant probability predicted by a CNN to a two-dimensional space, and then apply a spatial segmentation classifier trained on 2614 ultrasound images for prediction of thyroid nodule malignancy and guidance to radiologists.

RESULTS

The CReUMAP embedding correlates well with the TI-RADS categories of thyroid nodules. The parametric version that embeds external test dataset of 303 images in presence of the training data with known pathological diagnosis improves the benign and malignant nodule diagnostic accuracy (p-value = 0.016) and confidence (p-value = 1.902 × 10^-6) of eight radiologists of different experience levels significantly as well as their inter-observer agreements (kappa≥0.75). CReUMAP achieve 90.8% accuracy, 92.1% sensitivity and 88.6% specificity in test set.

CONCLUSION

CReUMAP embedding is well correlated with the pathological diagnosis of thyroid nodules, and helps radiologists achieve more accurate, confident and consistent diagnosis. It allows a medical center to generate its locally adapted embedding using an already-trained classification model in an updateable manner on an ever-growing local database as long as the extracted feature vectors and predicted diagnostic probabilities of the correspondent classification model can be outputted.

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss

-Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary clinical diagnosis. However, the majority of contemporary efforts exclusively focus on regression from input to binary labels, ignoring the relationship between visual features and semantic vectors of labels. In addition, there is an imbalance in data amount between diseases, which frequently causes intelligent diagnostic systems to make erroneous disease predictions. Therefore, we aim to improve the accuracy of the multi-label classification of chest X-ray images. Chest X-ray14 pictures were utilized as the multi-label dataset for the experiments in this study. By fine-tuning the ConvNeXt network, we got visual vectors, which we combined with semantic vectors encoded by BioBert to map the two different forms of features into a common metric space and made semantic vectors the prototype of each class in metric space. The metric relationship between images and labels is then considered from the image level and disease category level, respectively, and a new dual-weighted metric loss function is proposed. Finally, the average AUC score achieved in the experiment reached 0.826, and our model outperformed the comparison models.

Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing

Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it hard for intelligent CAD systems to deploy quickly; (ii) due to the personnel and information barriers, it is difficult for medical institutions to conduct the same pre-processing operations, making a deep model that performs well on a specific medical institution difficult to achieve similar performances on the same task in other medical institutions. To overcome these problems, we propose a deep-reinforcement-learning-based task-oriented homogenized automatic pre-processing (DRL-HAPre) framework to overcome these two problems. This framework utilizes deep reinforcement learning techniques to learn a policy network to automatically and adaptively select the optimal pre-processing operations for the input medical images according to different analysis tasks, thus helping the intelligent CAD system to achieve a rapid deployment (i.e., painless) and maintain a satisfactory performance (i.e., accurate) among different medical institutes. To verify the effectiveness and advantages of the proposed DRL-HAPre framework, we further develop a homogenized automatic pre-processing model based on the DRL-HAPre framework to realize the automatic pre-processing of key region selection (called HAPre-KRS) in the pneumonia image classification task. Extensive experimental studies are conducted on three pediatric pneumonia classification datasets with different image qualities, and the results show that: (i) There does exist a hard-to-reproduce problem in clinical practices and the fact that having different medical image qualities in different medical institutes is an important reason for the existing of hard-to-reproduce problem, so it is compelling to propose homogenized automatic pre-processing method. (ii) The proposed HAPre-KRS model and DRL-HAPre framework greatly outperform three kinds of state-of-the-art baselines (i.e., pre-processing, attention and pneumonia baseline), and the lower the medical image quality, the greater the improvements of using our HAPre-KRS model and DRL-HAPre framework. (iii) With the help of homogenized pre-processing, HAPre-KRS (and DRL-HAPre framework) can greatly avoid performance degradation in real-world cross-source applications (i.e., thus overcoming the hard-to-reproduce problem).

3D-MedTranCSGAN: 3D Medical Image Transformation using CSGAN

Computer vision techniques are a rapidly growing area of transforming medical images for various specific medical applications. In an end-to-end application, this paper proposes a 3D Medical Image Transformation Using a CSGAN model named a 3D-MedTranCSGAN. The 3D-MedTranCSGAN model is an integration of non-adversarial loss components and the Cyclic Synthesized Generative Adversarial Networks. The proposed model utilizes PatchGAN's discriminator network, to penalize the difference between the synthesized image and the original image. The model also computes the non-adversary loss functions such as content, perception, and style transfer losses. 3DCascadeNet is a new generator architecture introduced in the paper, which is used to enhance the perceptiveness of the transformed medical image by encoding-decoding pairs. We use the 3D-MedTranCSGAN model to do various tasks without modifying specific applications: PET to CT image transformation; reconstruction of CT to PET; modification of movement artefacts in MR images; and removing noise in PET images. We found that 3D-MedTranCSGAN outperformed other transformation methods in our experiments. For the first task, the proposed model yields SSIM is 0.914, PSNR is 26.12, MSE is 255.5, VIF is 0.4862, UQI is 0.9067 and LPIPs is 0.2284. For the second task, the model yields 0.9197, 25.7, 257.56, 0.4962, 0.9027, 0.2262. For the third task, the model yields 0.8862, 24.94, 0.4071, 0.6410, 0.2196. For the final task, the model yields 0.9521, 33.67, 33.57, 0.6091, 0.9255, 0.0244. Based on the result analysis, the proposed model outperforms the other techniques.

Hybrid transformer UNet for thyroid segmentation from ultrasound scans

Deep learning based medical image segmentation methods have been widely used for thyroid gland segmentation from ultrasound images, which is of great importance for the diagnosis of thyroid disease since it can provide various valuable sonography features. However, existing thyroid gland segmentation models suffer from: (1) low-level features that are significant in depicting thyroid boundaries are gradually lost during the feature encoding process, (2) contextual features reflecting the changes of difference between thyroid and other anatomies in the ultrasound diagnosis process are either omitted by 2D convolutions or weakly represented by 3D convolutions due to high redundancy. In this work, we propose a novel hybrid transformer UNet (H-TUNet) to segment thyroid glands in ultrasound sequences, which consists of two parts: (1) a 2D Transformer UNet is proposed by utilizing a designed multi-scale cross-attention transformer (MSCAT) module on every skipped connection of the UNet, so that the low-level features from different encoding layers are integrated and refined according to the high-level features in the decoding scheme, leading to better representation of differences between anatomies in one ultrasound frame; (2) a 3D Transformer UNet is proposed by applying a 3D self-attention transformer (SAT) module to the very bottom layer of 3D UNet, so that the contextual features representing visual differences between regions and consistencies within regions could be strengthened from successive frames in the video. The learning process of the H-TUNet is formulated as a unified end-to-end network, so the intra-frame feature extraction and inter-frame feature aggregation can be learned and optimized jointly. The proposed method was evaluated on Thyroid Segmentation in Ultrasonography Dataset (TSUD) and TG3k Dataset. Experimental results have demonstrated that our method outperformed other state-of-the-art methods with respect to the certain benchmarks for thyroid gland segmentation.

Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques

Given the increased interest in utilizing artificial intelligence as an assistive tool in the medical sector, colorectal polyp detection and classification using deep learning techniques has been an active area of research in recent years. The motivation for researching this topic is that physicians miss polyps from time to time due to fatigue and lack of experience carrying out the procedure. Unidentified polyps can cause further complications and ultimately lead to colorectal cancer (CRC), one of the leading causes of cancer mortality. Although various techniques have been presented recently, several key issues, such as the lack of enough training data, white light reflection, and blur affect the performance of such methods. This paper presents a survey on recently proposed methods for detecting polyps from colonoscopy. The survey covers benchmark dataset analysis, evaluation metrics, common challenges, standard methods of building polyp detectors and a review of the latest work in the literature. We conclude this paper by providing a precise analysis of the gaps and trends discovered in the reviewed literature for future work.

A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.

ASE-Net: A tumor segmentation method based on image pseudo enhancement and adaptive-scale attention supervision module

Fluorine 18(¹⁸F) fluorodeoxyglucose positron emission tomography and Computed Tomography (PET/CT) is the preferred imaging method of choice for the diagnosis and treatment of many cancers. However, factors such as low-contrast organ and tissue images, and the original scale of tumors pose huge obstacles to the accurate segmentation of tumors. In this work, we propose a novel model ASE-Net which is used for multimodality tumor segmentation. Firstly, we propose a pseudo-enhanced CT image generation method based on metabolic intensity to generate pseudo-enhanced CT images as additional input, which reduces the learning of the network in the spatial position of PET/CT and increases the discriminability of the corresponding structural positions of the high and low metabolic region. Second, unlike previous networks that directly segment tumors of all scales, we propose an Adaptive-Scale Attention Supervision Module at the skip connections, after combining the results of all paths, tumors of different scales will be given different receptive fields. Finally, Dual Path Block is used as the backbone of our network to leverage the ability of residual learning for feature reuse and dense connection for exploring new features. Our experimental results on two clinical PET/CT datasets demonstrate the effectiveness of our proposed network and achieve 78.56% and 72.57% in Dice Similarity Coefficient, respectively, which has better performance compared to state-of-the-art network models, whether for large or small tumors. The proposed model will help pathologists formulate more accurate diagnoses by providing reference opinions during diagnosis, consequently improving patient survival rate.

HD2A-Net: A novel dual gated attention network using comprehensive hybrid dilated convolutions for medical image segmentation

The convolutional neural networks (CNNs) have been widely proposed in the medical image analysis tasks, especially in the image segmentations. In recent years, the encoder-decoder structures, such as the U-Net, were rendered. However, the multi-scale information transmission and effective modeling for long-range feature dependencies in these structures were not sufficiently considered. To improve the performance of the existing methods, we propose a novel hybrid dual dilated attention network (HD²A-Net) to conduct the lesion region segmentations. In the proposed network, we innovatively present the comprehensive hybrid dilated convolution (CHDC) module, which facilitates the transmission of the multi-scale information. Based on the CHDC module and the attention mechanisms, we design a novel dual dilated gated attention (DDGA) block to enhance the saliency of related regions from the multi-scale aspect. Besides, a dilated dense (DD) block is designed to expand the receptive fields. The ablation studies were performed to verify our proposed blocks. Besides, the interpretability of the HD²A-Net was analyzed through the visualization of the attention weight maps from the key blocks. Compared to the state-of-the-art methods including CA-Net, DeepLabV3+, and Attention U-Net, the HD²A-Net outperforms significantly, with the metrics of Dice, Average Symmetric Surface Distance (ASSD), and mean Intersection-over-Union (mIoU) reaching 93.16%, 93.63%, and 94.72%, 0.36 pix, 0.69 pix, and 0.52 pix, and 88.03%, 88.67%, and 90.33% on three publicly available medical image datasets: MAEDE-MAFTOUNI (COVID-19 CT), ISIC-2018 (Melanoma Dermoscopy), and Kvasir-SEG (Gastrointestinal Disease Polyp), respectively.

Hybrid-scale contextual fusion network for medical image segmentation

Medical image segmentation result is an essential reference for disease diagnosis. Recently, with the development and application of convolutional neural networks, medical image processing has significantly developed. However, most existing automatic segmentation tasks are still challenging due to various positions, sizes, and shapes, resulting in poor segmentation performance. In addition, most of the current methods use the encoder-decoder architecture for feature extraction, focusing on the acquisition of semantic information but ignoring the specific target and global context information. In this work, we propose a hybrid-scale contextual fusion network to capture the richer spatial and semantic information. First, a hybrid-scale embedding layer (HEL) is employed before the transformer. By mixing each embedding with multiple patches, the object information of different scales can be captured availably. Further, we present a standard transformer to model long-range dependencies in the first two skip connections. Meanwhile, the pooling transformer (PTrans) is employed to handle long input sequences in the following two skip connections. By leveraging the global average pooling operation and the corresponding transformer block, the spatial structure information of the target will be learned effectively. In the last, dual-branch channel attention module (DCA) is proposed to focus on crucial channel features and conduct multi-level features fusion simultaneously. By utilizing the fusion scheme, richer context and fine-grained features are captured and encoded efficiently. Extensive experiments on three public datasets demonstrate that the proposed method outperforms state-of-the-art methods.

Wave-Net: A lightweight deep network for retinal vessel segmentation from fundus images

Accurate segmentation of retinal vessels from fundus images is fundamental for the diagnosis of numerous diseases of eye, and an automated vessel segmentation method can effectively help clinicians to make accurate diagnosis for the patients and provide the appropriate treatment schemes. It is important to note that both thick and thin vessels play the key role for disease judgements. Because of complex factors, the precise segmentation of thin vessels is still a great challenge, such as the presence of various lesions, image noise, complex backgrounds and poor contrast in the fundus images. Recently, because of the advantage of context feature representation learning capabilities, deep learning has shown a remarkable segmentation performance on retinal vessels. However, it still has some shortcomings on high-precision retinal vessel extraction due to some factors, such as semantic information loss caused by pooling operations, limited receptive field, etc. To address these problems, this paper proposes a new lightweight segmentation network for precise retinal vessel segmentation, which is called as Wave-Net model on account of the whole shape. To alleviate the influence of semantic information loss problem to thin vessels, to acquire more contexts about micro structures and details, a detail enhancement and denoising block (DED) is proposed to improve the segmentation precision on thin vessels, which replaces the simple skip connections of original U-Net. On the other hand, it could well alleviate the influence of the semantic gap problem. Further, faced with limited receptive field, for multi-scale vessel detection, a multi-scale feature fusion block (MFF) is proposed to fuse cross-scale contexts to achieve higher segmentation accuracy and realize effective processing of local feature maps. Experiments indicate that proposed Wave-Net achieves an excellent performance on retinal vessel segmentation while maintaining a lightweight network design compared to other advanced segmentation methods, and it also has shown a better segmentation ability to thin vessels.

TC-Net: A Transformer Capsule Network for EEG-based emotion recognition

Deep learning has recently achieved remarkable success in emotion recognition based on Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly used models. However, due to the local feature learning mechanism, CNNs have difficulty in capturing the global contextual information involving temporal domain, frequency domain, intra-channel and inter-channel. In this paper, we propose a Transformer Capsule Network (TC-Net), which mainly contains an EEG Transformer module to extract EEG features and an Emotion Capsule module to refine the features and classify the emotion states. In the EEG Transformer module, EEG signals are partitioned into non-overlapping windows. A Transformer block is adopted to capture global features among different windows, and we propose a novel patch merging strategy named EEG-PatchMerging (EEG-PM) to better extract local features. In the Emotion Capsule module, each channel of the EEG feature maps is encoded into a capsule to better characterize the spatial relationships among multiple features. Experimental results on two popular datasets (i.e., DEAP and DREAMER) demonstrate that the proposed method achieves the state-of-the-art performance in the subject-dependent scenario. Specifically, on DEAP (DREAMER), our TC-Net achieves the average accuracies of 98.76% (98.59%), 98.81% (98.61%) and 98.82% (98.67%) at valence, arousal and dominance dimensions, respectively. Moreover, the proposed TC-Net also shows high effectiveness in multi-state emotion recognition tasks using the popular VA and VAD models. The main limitation of the proposed model is that it tends to obtain relatively low performance in the cross-subject recognition task, which is worthy of further study in the future.

RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames

Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-invasive method that provides multi-modal images containing important information regarding the tumor. Many contemporary techniques employ four modalities: T1-weighted (T1), T1-weighted with contrast (T1c), T2-weighted (T2), and fluid-attenuation-inversion-recovery (FLAIR), each of which provides unique and important characteristics for the location of each tumor. Although several modern procedures provide decent segmentation results on the multimodal brain tumor image segmentation benchmark (BraTS) dataset, they lack performance when evaluated simultaneously on all the regions of MRI images. Furthermore, there is still room for improvement due to parameter limitations and computational complexity. Therefore, in this work, a novel encoder-decoder-based architecture is proposed for the effective segmentation of brain tumor regions. Data pre-processing is performed by applying N4 bias field correction, z-score, and 0 to 1 resampling to facilitate model training. To minimize the loss of location information in different modules, a residual spatial pyramid pooling (RASPP) module is proposed. RASPP is a set of parallel layers using dilated convolution. In addition, an attention gate (AG) module is used to efficiently emphasize and restore the segmented output from extracted feature maps. The proposed modules attempt to acquire rich feature representations by combining knowledge from diverse feature maps and retaining their local information. The performance of the proposed deep network based on RASPP, AG, and recursive residual (R2) block termed RAAGR2-Net is evaluated on the BraTS benchmarks. The experimental results show that the suggested network outperforms existing networks that exhibit the usefulness of the proposed modules for "fine" segmentation. The code for this work is made available online at: https://github.com/Rehman1995/RAAGR2-Net.

Quantifying imbalanced classification methods for leukemia detection

Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral blood in malignant cases. Early detection of the cell in bone marrow is essential as the disease progresses rapidly if left untreated. However, automated classification of the cell is challenging, owing to its fine-grained variability with B-lymphoid precursor cells and imbalanced data points. Deep learning algorithms demonstrate potential for such fine-grained classification as well as suffer from the imbalanced class problem. In this paper, we explore different deep learning-based State-Of-The-Art (SOTA) approaches to tackle imbalanced classification problems. Our experiment includes input, GAN (Generative Adversarial Networks), and loss-based methods to mitigate the issue of imbalanced class on the challenging C-NMC and ALLIDB-2 dataset for leukemia detection. We have shown empirical evidence that loss-based methods outperform GAN-based and input-based methods in imbalanced classification scenarios.

RV-ESA: A novel computer-aided elastic shape analysis system for retinal vessels in diabetic retinopathy

Diabetic retinopathy (DR), one of the most common and serious complications of diabetes, has become one of the main blindness diseases. The retinal vasculature is the only part of the human circulatory system that allows direct noninvasive visualization of the body's microvasculature, which provides the opportunity to detect the structural and functional changes before DR becomes unable to intervene. For decades, as the fundamental step in computer-assisted analysis of retinopathy, retinal vascular extraction methods have been largely developed. However, further research focusing on retinal vascular analysis is still in its infancy. Meanwhile, due to the complexity of retinal vascular structure, the relationship between vascular geometry and DR has never been concluded. This paper aims to provide a novel computer-aided shape analysis system for retinal vessels. To perform retinal vascular shape analysis, a mathematical geometric representation is firstly generated by utilizing the proposed shape modeling method. Then, several useful statistical tools (e.g. Graph Mean, Graph PCA) are adopted to quantitatively analyze the vascular shape. Besides, in order to visualize the changes in vascular shape in the progression of DR, a geodesic tool is used to display the deformation process for ophthalmologists to observe. The efficacy of this analysis system is demonstrated in the EyePACS dataset and the subsequent visit records of 98 patients from the proprietary dataset. The experimental results show that there is a certain correlation between the variation of retinal vascular shape and DR progression, and the Graph PCA scores of retinal vessels are negatively correlated with DR grades. The code of our RV-ESA system can be publicly available at github.com/XiaolingLuo/RV-ESA.

Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detector

Periodontitis is a serious oral disease that can lead to severe conditions such as bone loss and teeth falling out if left untreated. Diagnosis of radiographic bone loss (RBL) is critical for the staging and treatment of periodontitis. Unfortunately, the RBL diagnosis by examining the panoramic radiographs is time-consuming. The demand for automated image analysis is urgent. However, existing deep learning methods have limited performances in diagnosis accuracy and have certain difficulties in implementation. Hence, we propose a novel two-stage periodontitis detection convolutional neural network (PDCNN), where we optimize the detector with an anchor-free encoding that allows fast and accurate prediction. We also introduce a proposal-connection module in our detector that excludes less relevant regions of interests (ROIs), making the network focus on more relevant ROIs to improve detection accuracy. Furthermore, we introduced a large-scale, high-resolution panoramic radiograph dataset that captures various complex cases with professional periodontitis annotations. Experiments on our panoramic-image dataset show that the proposed approach achieved an RBL classification accuracy of 0.762. This result shows that our approach outperforms state-of-the-art detectors such as Faster R-CNN and YOLO-v4. We can conclude that the proposed method successfully improves the RBL detection performance. The dataset and our code have been released on GitHub. (https://github.com/PuckBlink/PDCNN).

FusionSegNet: Fusing global foot features and local wound features to diagnose diabetic foot

The Diabetic Foot (DF) is threatening every diabetic patient's health. Every year, more than one million people suffer amputation in the world due to lack of timely diagnosis of DF. Diagnosing DF at early stage is very essential to improve the survival rate and quality of patients. However, it is easy for inexperienced doctors to confuse DFU wounds and other specific ulcer wounds when there is a lack of patients' health records in underdeveloped areas. It is of great value to distinguish diabetic foot ulcer from chronic wounds. And the characteristics of deep learning can be well applied in this field. In this paper, we propose the FusionSegNet fusing global foot features and local wound features to identify DF images from foot ulcer images. In particular, we apply a wound segmentation module to segment foot ulcer wounds, which guides the network to pay attention to wound area. T he FusionSegNet combines two kinds of features to make a final prediction. Our method is evaluated upon our dataset collected by Shanghai Municipal Eighth People's Hospital in clinical environment. In the training-validation stage, we collect 1211 images for a 5-fold cross-validation. Our method can classify DF images and non-DF images with the area under the receiver operating characteristic curve (AUC) value of 98.93%, accuracy of 95.78%, sensitivity of 94.27%, specificity of 96.88%, and F1-score of 94.91%. With the excellent performance, the proposed method can accurately extract wound features and greatly improve the classification performance. In general, the method proposed in this paper can help clinicians make more accurate judgments of diabetic foot and has great potential in clinical auxiliary diagnosis.

Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions' segmentation and classification tasks, which consists of a segmentation network and a classification network. To improve the performance of the segmentation network, we propose a novel module of Multi-Scale Holistic Feature Exploration (MSH) to thoroughly exploit perceptual clues latent among multi-scale feature maps as synthesized by the decoder. The MSH module enables holistic exploration of features across multiple scales to more effectively support downstream image analysis tasks. To boost the performance of the classification network, we propose a novel module of Cross-Modality Collaborative Feature Exploration (CMC) to discover latent discriminative features by collaboratively exploiting potential relationships between cross-modal features of dermoscopic images and clinical metadata. The CMC module enables dynamically capturing versatile interaction effects among cross-modal features during the model's representation learning procedure by discriminatively and adaptively learning the interaction weight associated with each crossmodality feature pair. In addition, to effectively reduce background noise and boost the lesion discrimination ability of the classification network, we crop the images based on lesion masks generated by the best segmentation model. We evaluate the proposed pipeline on the four public skin lesion datasets, where the ISIC 2018 and PH2 are for segmentation, and the ISIC 2019 and ISIC 2020 are combined into a new dataset, ISIC 2019&2020, for classification. It achieves a Jaccard index of 83.31% and 90.14% in skin lesion segmentation, an AUC of 97.98% and an Accuracy of 92.63% in skin lesion classification, which is superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Last but not least, the new model for segmentation utilizes much fewer model parameters (3.3 M) than its peer approaches, leading to a greatly reduced number of labeled samples required for model training, which obtains substantially stronger robustness than its peers.

Tuberculous pleural effusion prediction using ant colony optimizer with grade-based search assisted support vector machine

Introduction

Although tuberculous pleural effusion (TBPE) is simply an inflammatory response of the pleura caused by tuberculosis infection, it can lead to pleural adhesions and cause sequelae of pleural thickening, which may severely affect the mobility of the chest cavity.

Methods

In this study, we propose bGACO-SVM, a model with good diagnostic power, for the adjunctive diagnosis of TBPE. The model is based on an enhanced continuous ant colony optimization (ACOR) with grade-based search technique (GACO) and support vector machine (SVM) for wrapped feature selection. In GACO, grade-based search greatly improves the convergence performance of the algorithm and the ability to avoid getting trapped in local optimization, which improves the classification capability of bGACO-SVM.

Results

To test the performance of GACO, this work conducts comparative experiments between GACO and nine basic algorithms and nine state-of-the-art variants as well. Although the proposed GACO does not offer much advantage in terms of time complexity, the experimental results strongly demonstrate the core advantages of GACO. The accuracy of bGACO-predictive SVM was evaluated using existing datasets from the UCI and TBPE datasets.

Discussion

In the TBPE dataset trial, 147 TBPE patients were evaluated using the created bGACO-SVM model, showing that the bGACO-SVM method is an effective technique for accurately predicting TBPE.

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

Introduction

Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.

Methods

Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.

Results

To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.

Discussion

The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

Impact of quarantine on fractional order dynamical model of Covid-19

In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease.

Asymmetry-enhanced attention network for Alzheimer's diagnosis with structural Magnetic Resonance Imaging

BACKGROUND AND OBJECTIVE

With the aging of the global population becoming severe, Alzheimer's disease (AD) has become one of the world's most common senile diseases. Many studies have suggested that the brain's left-right asymmetry is one of the possible diagnostic landmarks for AD. However, most published approaches to classification problems may not adequately explore the asymmetry between the left and right hemispheres. At the same time, the relationship between asymmetry traits and other classifier features remains understudied.

METHODS

In this paper, we proposed an asymmetry enhanced attention network (ASEAN) for AD diagnosis that effectively combines the anatomical asymmetry characteristics of the brain to enhance the accuracy and stability of classification tasks. First, we proposed a multi-scale asymmetry feature extraction module (MSAF) that can extract the asymmetry features of the brain from various scales. Second, we proposed an asymmetry refinement module (ARM) that considers the dependency between feature maps to suppress the irrelevant regions of the asymmetric feature maps. In addition, a parameter-free attention module was introduced to infer 4D attention weights and improve the network's representation capability.

RESULTS

The proposed method achieved performance improvements on two databases: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL). For the classification tasks on ADNI, the proposed method achieves 92.1% accuracy, 96.2% sensitivity, and 91.3% specificity on the AD vs. CN (Cognitively Normal) task. Compared with state-of-the-art methods, the proposed method could achieve comparable results.

CONCLUSION

The proposed model can extract long-range left-right brain similarity as complementary information and improve the model's diagnostic performance. A large number of experiments also support the model's validity. At the same time, this work provides a valuable reference for other neurological diseases, particularly those that exhibit left-right brain asymmetry during development.

Class-Aware Attention Network for infectious keratitis diagnosis using corneal photographs

Infectious keratitis is one of the common ophthalmic diseases and also one of the main blinding eye diseases in China, hence rapid and accurate diagnosis and treatment for infectious keratitis are urgent to prevent the progression of the disease and limit the degree of corneal injury. Unfortunately, the traditional manual diagnosis accuracy is usually unsatisfactory due to the indistinguishable visual features. In this paper, we propose a novel end-to-end fully convolutional network, named Class-Aware Attention Network (CAA-Net), for automatically diagnosing infectious keratitis (normal, viral keratitis, fungal keratitis, and bacterial keratitis) using corneal photographs. In CAA-Net, a class-aware classification module is first trained to learn class-related discriminative features using separate branches for each class. Then, the learned class-aware discriminative features are fed into the main branch and fused with other feature maps using two attention strategies to assist the final multi-class classification performance. For the experiments, we have built a new corneal photograph dataset with 1886 images from 519 patients and conducted comprehensive experiments to verify the effectiveness of our proposed method. The code is available at https://github.com/SWF-hao/CAA-Net_Pytorch.

A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images

BACKGROUND

One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis.

PURPOSE

To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results.

METHOD

The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning.

THE RESULTS

The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.

Dynamic changes in spatiotemporal transcriptome reveal maternal immune dysregulation of autism spectrum disorder

Maternal immune activation (MIA) during pregnancy is known to be an environmental risk factor for neurodevelopment and autism spectrum disorder (ASD). However, it is unclear at which fetal brain developmental windows and regions MIA induces ASD-related neurodevelopmental transcriptional abnormalities. The non-chasm differentially expressed genes (DEGs) involved in MIA inducing ASD during fetal brain developmental windows were identified by performing the differential expression analysis and comparing the common DEGs among MIA at four different gestational development windows, ASD with multiple brain regions from human patients and mouse models, and human and mouse embryonic brain developmental trajectory. The gene set and functional enrichment analyses were performing to identify MIA dysregulated ASD-related the fetal neurodevelopmental windows and brain regions and function annotations. Additionally, the networks were constructed using Cytoscape for visualization. MIA at E12.5 and E14.5 increased the risk of distinct brain regions for ASD. MIA-driven transcriptional alterations of non-chasm DEGs, during the coincidence brain developmental windows between human and mice, involving ASD-relevant synaptic components, as well as immune- and metabolism-related functions and pathways. Furthermore, a great number of non-chasm brain development-, immune-, and metabolism-related DEGs were overlapped in at least two existing ASD-associated databases, suggesting that the others could be considered as the candidate targets to construct the model mice for explaining the pathological changes of ASD when environmental factors (MIA) and gene mutation effects co-occur. Overall, our search supported that transcriptome-based MIA dysregulated the brain development-, immune-, and metabolism-related non-chasm DEGs at specific embryonic brain developmental window and region, leading to abnormal embryonic neurodevelopment, to induce the increasing risk of ASD.

Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning

Hepatocellular carcinoma (LIHC) is the fifth common cancer worldwide, and it requires effective diagnosis and treatment to prevent aggressive metastasis. The purpose of this study was to construct a machine learning-based diagnostic model for the diagnosis of liver cancer. Using weighted correlation network analysis (WGCNA), univariate analysis, and Lasso-Cox regression analysis, protein-protein interactions network analysis is used to construct gene networks from transcriptome data of hepatocellular carcinoma patients and find hub genes for machine learning. The five models, including gradient boosting, random forest, support vector machine, logistic regression, and integrated learning, were to identify a multigene prediction model of patients. Immunological assessment, TP53 gene mutation and promoter methylation level analysis, and KEGG pathway analysis were performed on these groups. Potential drug molecular targets for the corresponding hepatocellular carcinomas were obtained by molecular docking for analysis, resulting in the screening of 2 modules that may be relevant to the survival of hepatocellular carcinoma patients, and the construction of 5 diagnostic models and multiple interaction networks. The modes of action of drug-molecule interactions that may be effective against hepatocellular carcinoma core genes CCNA2, CCNB1, and CDK1 were investigated. This study is expected to provide research ideas for early diagnosis of hepatocellular carcinoma.

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

Background

Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis.

Methods

The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs.

Results

In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs.

Conclusions

We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.

SIL-Net: A Semi-Isotropic L-shaped network for dermoscopic image segmentation

BACKGROUND

Dermoscopic image segmentation using deep learning algorithms is a critical technology for skin cancer detection and therapy. Specifically, this technology is a spatially equivariant task and relies heavily on Convolutional Neural Networks (CNNs), which lost more effective features during cascading down-sampling or up-sampling. Recently, vision isotropic architecture has emerged to eliminate cascade procedures in CNNs as well as demonstrates superior performance. Nevertheless, it cannot be used for the segmentation task directly. Based on these discoveries, this research intends to explore an efficient architecture which not only preserves the advantages of the isotropic architecture but is also suitable for clinical dermoscopic diagnosis.

METHODS

In this work, we introduce a novel Semi-Isotropic L-shaped network (SIL-Net) for dermoscopic image segmentation. First, we propose a Patch Embedding Weak Correlation (PEWC) module to address the issue of no interaction between adjacent patches during the standard Patch Embedding process. Second, a plug-and-play and zero-parameter Residual Spatial Mirror Information (RSMI) path is proposed to supplement effective features during up-sampling and optimize the lesion boundaries. Third, to further reconstruct deep features and get refined lesion regions, a Depth Separable Transpose Convolution (DSTC) based up-sampling module is designed.

RESULTS

The proposed architecture obtains state-of-the-art performance on dermoscopy benchmark datasets ISIC-2017, ISIC-2018 and PH2. Respectively, the Dice coefficient (DICE) of above datasets achieves 89.63%, 93.47%, and 95.11%, where the Mean Intersection over Union (MIoU) are 82.02%, 88.21%, and 90.81%. Furthermore, the robustness and generalizability of our method has been demonstrated through additional experiments on standard intestinal polyp datasets (CVC-ClinicDB and Kvasir-SEG).

CONCLUSION

Our findings demonstrate that SIL-Net not only has great potential for precise segmentation of the lesion region but also exhibits stronger generalizability and robustness, indicating that it meets the requirements for clinical diagnosis. Notably, our method shows state-of-the-art performance on all five datasets, which highlights the effectiveness of the semi-isotropic design mechanism.

Multi-threshold image segmentation for melanoma based on Kapur’s entropy using enhanced ant colony optimization

Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain much information. However, the percentage of the critical information in the image is small, and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional histogram approach to the above problem. We present an enhanced ant colony optimization for continuous domains (EACOR) in the proposed model based on the soft besiege and chase strategies. Further, EACOR is combined with two-dimensional Kapur’s entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also conducted several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.

Modality-correlation embedding model for breast tumor diagnosis with mammography and ultrasound images

The fusion of mammography and ultrasound images helps to improve tumor classification accuracy. However, traditional fusion models ignore the correlation between these two modalities, resulting in limited performance improvement. To address this problem, a modality-correlation embedding model was proposed for breast tumor diagnosis by combining mammography and ultrasound imaging. By jointly optimizing the correlation between mammography and ultrasound and classification loss of individual modalities, two mappings are learned to project mammography and ultrasound from their original feature spaces into a common label space. A novel modality-correlation term is introduced to maintain the pairwise closeness of multimodal data in the common label space. Contrary to previous studies that did not consider the correlation between multimodal data, the proposed term can exploit the learned correlation information in the fusion process, which guarantees the consistency of the diagnosis results of multimodal images from the same patient. The proposed method was evaluated on our dataset, which contained ultrasound and mammography images from 73 patients. The area under the ROC curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 95.83, 95, 91.67, 95.83, 95.83, and 88.89%, respectively. The experimental results also demonstrate that the proposed method outperforms traditional fusion methods.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

HSNet: A hybrid semantic network for polyp segmentation

Automatic polyp segmentation can help physicians to effectively locate polyps (a.k.a. region of interests) in clinical practice, in the way of screening colonoscopy images assisted by neural networks (NN). However, two significant bottlenecks hinder its effectiveness, disappointing physicians' expectations. (1) Changeable polyps in different scaling, orientation, and illumination, bring difficulty in accurate segmentation. (2) Current works building on a dominant decoder-encoder network tend to overlook appearance details (e.g., textures) for a tiny polyp, degrading the accuracy to differentiate polyps. For alleviating the bottlenecks, we investigate a hybrid semantic network (HSNet) that adopts both advantages of Transformer and convolutional neural networks (CNN), aiming at improving polyp segmentation. Our HSNet contains a cross-semantic attention module (CSA), a hybrid semantic complementary module (HSC), and a multi-scale prediction module (MSP). Unlike previous works on segmenting polyps, we newly insert the CSA module, which can fill the gap between low-level and high-level features via an interactive mechanism that exchanges two types of semantics from different NN attentions. By a dual-branch structure of Transformer and CNN, we newly design an HSC module, for capturing both long-range dependencies and local details of appearance. Besides, the MSP module can learn weights for fusing stage-level prediction masks of a decoder. Experimentally, we compared our work with 10 state-of-the-art works, including both recent and classical works, showing improved accuracy (via 7 evaluative metrics) over 5 benchmark datasets, e.g., it achieves 0.926/0.877 mDic/mIoU on Kvasir-SEG, 0.948/0.905 mDic/mIoU on ClinicDB, 0.810/0.735 mDic/mIoU on ColonDB, 0.808/0.74 mDic/mIoU on ETIS, and 0.903/0.839 mDic/mIoU on Endoscene. The proposed model is available at (https://github.com/baiboat/HSNet).

A Cuproptosis Activation Scoring model predicts neoplasm-immunity interactions and personalized treatments in glioma

Gliomas are malignant tumors in the central nervous system. Cuproptosis is a newly discovered cell death mechanism targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that cuproptosis participates in tumor progression, but its role in gliomas is still elusive. Here, we systematically explored the bulk-tumor and single-cell transcriptome data to reveal its role in gliomas. The cuproptosis activity score (CuAS) was constructed based on cuproptosis-related genes, and machine learning techniques validated the score stability. High CuAS gliomas were more likely to have a poor prognosis and an aggressive mesenchymal (MES) subtype. Subsequently, the SCENIC algorithm predicted 20 CuAS-related transcription factors (TFs) in gliomas. Function enrichment and microenvironment analyses found that CuAS was associated with tumor immune infiltration. Accordingly, intercellular communications between neoplasm and immunity were explored by the R package "Cellchat". Five signaling pathways and 8 ligand-receptor pairs including ICAM1, ITGAX, ITGB2, ANXA1-FRR1, and the like, were identified to suggest how cuproptosis activity connected neoplastic and immune cells. Critically, 13 potential drugs targeting high CuAs gliomas were predicted according to the CTRP and PRISM databases, including oligomycin A, dihydroartemisinin, and others. Taken together, cuproptosis is involved in glioma aggressiveness, neoplasm-immune interactions, and may be used to assist in drug selection.

Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets

Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's eye for detecting the damage caused to blood vessels is a part of the computer-aided diagnosis that has immensely progressed over the past few years due to the advent and success of deep learning. The challenges related to imbalanced datasets, inconsistent annotations, less number of sample images and inappropriate performance evaluation metrics has caused an adverse impact on the performance of the deep learning models. In order to tackle the effect caused by class imbalance, we have done extensive comparative analysis between various state-of-the-art methods on three benchmark datasets of diabetic retinopathy: - Kaggle DR detection, IDRiD and DDR, for classification, object detection and segmentation tasks. This research could serve as a concrete baseline for future research in this field to find appropriate approaches and deep learning architectures for imbalanced datasets.

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.

Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features

Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.