Detecting prostate cancer using deep learning convolution neural network with transfer learning approach

Identification and Localization of Indolent and Aggressive Prostate Cancers Using Multilevel Bi-LSTM

Identifying indolent and aggressive prostate cancers is a critical problem for optimal treatment. The existing approaches of prostate cancer detection are facing challenges as the techniques rely on ground truth labels with limited accuracy, and histological similarity, and do not consider the disease pathology characteristics, and indefinite differences in appearance between the cancerous and healthy tissue lead to many false positive and false negative interpretations. Hence, this research introduces a comprehensive framework designed to achieve accurate identification and localization of prostate cancers, irrespective of their aggressiveness. This is accomplished through the utilization of a sophisticated multilevel bidirectional long short-term memory (Bi-LSTM) model. The pre-processed images are subjected to multilevel feature map-based U-Net segmentation, bolstered by ResNet-101 and a channel-based attention module that improves the performance. Subsequently, segmented images undergo feature extraction, encompassing various feature types, including statistical features, a global hybrid-based feature map, and a ResNet-101 feature map that enhances the detection accuracy. The extracted features are fed to the multilevel Bi-LSTM model, further optimized through channel and spatial attention mechanisms that offer the effective localization and recognition of complex structures of cancer. Further, the framework represents a promising approach for enhancing the diagnosis and localization of prostate cancers, encompassing both indolent and aggressive cases. Rigorous testing on a distinct dataset demonstrates the model's effectiveness, with performance evaluated through key metrics which are reported as 96.72%, 96.17%, and 96.17% for accuracy, sensitivity, and specificity respectively utilizing the dataset 1. For dataset 2, the model achieves the accuracy, sensitivity, and specificity values of 94.41%, 93.10%, and 94.96% respectively. These results surpass the efficiency of alternative methods.

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.

Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions

Radiological image analysis using machine learning has been extensively applied to enhance biopsy diagnosis accuracy and assist radiologists with precise cures. With improvements in the medical industry and its technology, computer-aided diagnosis (CAD) systems have been essential in detecting early cancer signs in patients that could not be observed physically, exclusive of introducing errors. CAD is a detection system that combines artificially intelligent techniques with image processing applications thru computer vision. Several manual procedures are reported in state of the art for cancer diagnosis. Still, they are costly, time-consuming and diagnose cancer in late stages such as CT scans, radiography, and MRI scan. In this research, numerous state-of-the-art approaches on multi-organs detection using clinical practices are evaluated, such as cancer, neurological, psychiatric, cardiovascular and abdominal imaging. Additionally, numerous sound approaches are clustered together and their results are assessed and compared on benchmark datasets. Standard metrics such as accuracy, sensitivity, specificity and false-positive rate are employed to check the validity of the current models reported in the literature. Finally, existing issues are highlighted and possible directions for future work are also suggested.

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%-98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays

Background

The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer's aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA).

Methods

Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label.

Results

Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98.

Conclusion

Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.

A survey on cancer detection via convolutional neural networks: Current challenges and future directions

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection.

Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images

The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics.

A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI

BACKGROUND

Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa and benign conditions (BCs) on magnetic resonance imaging (MRI).

METHODS

In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two institutions were included and divided into training (n = 309), internal validation (n = 200), and external validation (n = 200) cohorts. A transfer learning signature (TLS) that was pretrained with the whole slide images of PCa and fine-tuned on prebiopsy MRI images was constructed. A TLN that integrated the TLS, the Prostate Imaging-Reporting and Data System (PI-RADS) score, and the clinical factor was developed by multivariate logistic regression. The performance of the TLS, clinical model (CM), and TLN were evaluated in the validation cohorts using the receiver operating characteristic (ROC) curve, the Delong test, the integrated discrimination improvement (IDI), and decision curve analysis.

RESULTS

TLS, PI-RADS score, and age were selected for TLN construction. The TLN yielded areas under the curve of 0.9757 (95% CI, 0.9613-0.9902), 0.9255 (95% CI, 0.8873-0.9638), and 0.8766 (95% CI, 0.8267-0.9264) in the training, internal validation, and external validation cohorts, respectively, for the discrimination of PCa and BCs. The TLN outperformed the TLS and the CM in both the internal and external validation cohorts. The decision curve showed that the TLN added more net benefit than the CM.

CONCLUSIONS

The proposed TLN has the potential to be used as a noninvasive tool for PCa and BCs differentiation.

A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.

Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone

Purpose

Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier for patients in the prostate-specific antigen gray zone are to be developed and compared, identifying valuable predictors. Predictive models are to be integrated into actual clinical decisions.

Methods

Patient information was collected from December 01, 2014 to December 01, 2022 from the Department of Urology, The First Affiliated Hospital of Nanchang University. Patients with a pathological diagnosis of prostate hyperplasia or prostate cancer (any PCa) and having a prostate-specific antigen (PSA) level of 4-10 ng/mL before prostate puncture were included in the initial information collection. Eventually, 756 patients were selected. Age, total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), fPSA/tPSA, prostate volume (PV), prostate-specific antigen density (PSAD), (fPSA/tPSA)/PSAD, and the prostate MRI results of these patients were recorded. After univariate and multivariate logistic analyses, statistically significant predictors were screened to build and compare machine learning models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier to determine more valuable predictors.

Results

Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier exhibit higher predictive power than individual metrics. The area under the curve (AUC) (95% CI), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of the LogisticRegression machine learning prediction model were 0.932 (0.881-0.983), 0.792, 0.824, 0.919, 0.652, 0.920, and 0.728, respectively; of the XGBoost machine learning prediction model were 0.813 (0.723-0.904), 0.771, 0.800, 0.768, 0.737, 0.793 and 0.767, respectively; of the GaussianNB machine learning prediction model were 0.902 (0.843-0.962), 0.813, 0.875, 0.819, 0.600, 0.909, and 0.712, respectively; and of the LGBMClassifier machine learning prediction model were 0.886 (0.809-0.963), 0.833, 0.882, 0.806, 0.725, 0.911, and 0.796, respectively. The LogisticRegression machine learning prediction model has the highest AUC among all prediction models, and the difference between the AUC of the LogisticRegression prediction model and those of XGBoost, GaussianNB, and LGBMClassifier is statistically significant (p < 0.001).

Conclusion

Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier algorithms exhibit superior predictability for patients in the PSA gray area, with the LogisticRegression model yielding the best prediction. The aforementioned predictive models can be used for actual clinical decision-making.​.

Prostate lesion segmentation based on a 3D end-to-end convolution neural network with deep multi-scale attention

Prostate cancer is one of the deadest cancers among human beings. To better diagnose the prostate cancer, prostate lesion segmentation becomes a very important work, but its progress is very slow due to the prostate lesions small in size, irregular in shape, and blurred in contour. Therefore, automatic prostate lesion segmentation from mp-MRI is a great significant work and a challenging task. However, the most existing multi-step segmentation methods based on voxel-level classification are time-consuming, may introduce errors in different steps and lead to error accumulation. To decrease the computation time, harness richer 3D spatial features, and fuse the multi-level contextual information of mp-MRI, we present an automatic segmentation method in which all steps are optimized conjointly as one step to form our end-to-end convolutional neural network. The proposed end-to-end network DMSA-V-Net consists of two parts: (1) a 3D V-Net is used as the backbone network, it is the first attempt in employing 3D convolutional neural network for CS prostate lesion segmentation, (2) a deep multi-scale attention mechanism is introduced into the 3D V-Net which can highly focus on the ROI while suppressing the redundant background. As a merit, the attention can adaptively re-align the context information between the feature maps at different scales and the saliency maps in high-levels. We performed experiments based on five cross-fold validation with data including 97 patients. The results show that the Dice and sensitivity are 0.7014 and 0.8652 respectively, which demonstrates that our segmentation approach is more significant and accurate compared to other methods.

Computational Intelligence in Cancer Diagnostics: A Contemporary Review of Smart Phone Apps, Current Problems, and Future Research Potentials

Cancer is a dangerous and sometimes life-threatening disease that can have several negative consequences for the body, is a leading cause of mortality, and is becoming increasingly difficult to detect. Each form of cancer has its own set of traits, symptoms, and therapies, and early identification and management are important for a positive prognosis. Doctors utilize a variety of approaches to detect cancer, depending on the kind and location of the tumor. Imaging tests such as X-rays, Computed Tomography scans, Magnetic Resonance Imaging scans, and Positron Emission Tomography (PET) scans, which may provide precise pictures of the body's interior structures to spot any abnormalities, are some of the tools that doctors use to diagnose cancer. This article evaluates computational-intelligence approaches and provides a means to impact future work by focusing on the relevance of machine learning and deep learning models such as K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Deep Neural Network, Deep Boltzmann machine, and so on. It evaluates information from 114 studies using Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This article explores the advantages and disadvantages of each model and provides an outline of how they are used in cancer diagnosis. In conclusion, artificial intelligence shows significant potential to enhance cancer imaging and diagnosis, despite the fact that there are a number of clinical issues that need to be addressed.

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application

BACKGROUND

Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer.

METHODS

In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings.

RESULTS

The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.

An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning

This study proposes an electrocardiogram (ECG) signal stitching scheme to detect arrhythmias in drivers during driving. When the ECG is measured through the steering wheel during driving, the data are always exposed to noise caused by vehicle vibrations, bumpy road conditions, and the driver's steering wheel gripping force. The proposed scheme extracts stable ECG signals and transforms them into full 10 s ECG signals to classify arrhythmias using convolutional neural networks (CNN). Before the ECG stitching algorithm is applied, data preprocessing is performed. To extract the cycle from the collected ECG data, the R peaks are found and the TP interval segmentation is applied. An abnormal P peak is very difficult to find. Therefore, this study also introduces a P peak estimation method. Finally, 4 × 2.5 s ECG segments are collected. To classify arrhythmias with stitched ECG data, each time series' ECG signal is transformed via the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and transfer learning is performed for classification using CNNs. Finally, the parameters of the networks that provide the best performance are investigated. According to the classification accuracy, GoogleNet with the CWT image set shows the best results. The classification accuracy is 82.39% for the stitched ECG data, while it is 88.99% for the original ECG data.

Mansouri R. Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging

Prostate cancer (PCa) is becoming one of the most frequently occurring cancers among men and causes an even greater number of deaths. Due to the complexity of tumor masses, radiologists find it difficult to identify PCa accurately. Over the years, several PCa-detecting methods have been formulated, but these methods cannot identify cancer efficiently. Artificial Intelligence (AI) has both information technologies that simulate natural or biological phenomena and human intelligence in addressing issues. AI technologies have been broadly implemented in the healthcare domain, including 3D printing, disease diagnosis, health monitoring, hospital scheduling, clinical decision support, classification and prediction, and medical data analysis. These applications significantly boost the cost-effectiveness and accuracy of healthcare services. This article introduces an Archimedes Optimization Algorithm with Deep Learning-based Prostate Cancer Classification (AOADLB-P2C) model on MRI images. The presented AOADLB-P2C model examines MRI images for the identification of PCa. To accomplish this, the AOADLB-P2C model performs pre-processing in two stages: adaptive median filtering (AMF)-based noise removal and contrast enhancement. Additionally, the presented AOADLB-P2C model extracts features via a densely connected network (DenseNet-161) model with a root-mean-square propagation (RMSProp) optimizer. Finally, the presented AOADLB-P2C model classifies PCa using the AOA with a least-squares support vector machine (LS-SVM) method. The simulation values of the presented AOADLB-P2C model are tested using a benchmark MRI dataset. The comparative experimental results demonstrate the improvements of the AOADLB-P2C model over other recent approaches.

Automatic diagnosis and grading of Prostate Cancer with weakly supervised learning on whole slide images

BACKGROUND

The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations.

METHODS

A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model.

RESULTS

The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations.

CONCLUSION

The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (>0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.

A deep learning network for Gleason grading of prostate biopsies using EfficientNet

Objectives

The most crucial part in the diagnosis of cancer is severity grading. Gleason’s score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.

Methods

In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.

Result

To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.

Conclusions

The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal

Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can get around this issue. In order to diagnose prostate cancer via PA spectrum assessment, this work describes the machine learning (ML) technique implementation, such as supervised classification and unsupervised hierarchical clustering. The collected PA signals were preprocessed using Pwelch method, and the features are extracted using two methods such as hierarchical cluster and correlation assessment. The extracted features are classified using four ML-methods, namely, Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA). Furthermore, as these components alter throughout the progression of prostate cancer, this study focuses on the composition and distribution of collagen, lipids, and haemoglobin. In diseased tissues compared to normal tissues, there is a stronger correlation between the various chemical components ultrasonic power spectra, suggesting that the microstructural dispersion in tumour tissues has been more uniform. The accuracy of several classifiers used in cancer tissue diagnosis was greater than 94% for all four methods, which is effective than that of benchmark medical methods. Thus, the method shows significant promise for the noninvasive, early detection of severe prostate cancer.

Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

Hierarchical scale convolutional neural network for facial expression recognition

Recognition of facial expressions plays an important role in understanding human behavior, classroom assessment, customer feedback, education, business, and many other human-machine interaction applications. Some researchers have realized that using features corresponding to different scales can improve the recognition accuracy, but there is a lack of a systematic study to utilize the scale information. In this work, we proposed a hierarchical scale convolutional neural network (HSNet) for facial expression recognition, which can systematically enhance the information extracted from the kernel, network, and knowledge scale. First, inspired by that the facial expression can be defined by different size facial action units and the power of sparsity, we proposed dilation Inception blocks to enhance kernel scale information extraction. Second, to supervise relatively shallow layers for learning more discriminated features from different size feature maps, we proposed a feature guided auxiliary learning approach to utilize high-level semantic features to guide the shallow layers learning. Last, since human cognitive ability can progressively be improved by learned knowledge, we mimicked such ability by knowledge transfer learning from related tasks. Extensive experiments on lab-controlled, synthesized, and in-the-wild databases showed that the proposed method substantially boosts performance, and achieved state-of-the-art accuracy on most databases. Ablation studies proved the effectiveness of modules in the proposed method.

Preoperative AI-Driven Fluorescence Diagnosis of Non-Melanoma Skin Cancer

The diagnosis and treatment of non-melanoma skin cancer remain urgent problems. Histological examination of biopsy material—the gold standard of diagnosis—is an invasive procedure that requires a certain amount of time to perform. The ability to detect abnormal cells using fluorescence spectroscopy (FS) has been shown in many studies. This technique is rapidly expanding due to its safety, relative cost-effectiveness, and efficiency. However, skin lesion FS-based diagnosis is challenging due to a number of single overlapping spectra emitted by fluorescent molecules, making it difficult to distinguish changes in the overall spectrum and the molecular basis for it. We applied deep learning (DL) algorithms to quantitatively assess the ability of FS to differentiate between pathologies and normal skin. A total of 137 patients with various forms of primary and recurrent basal cell carcinoma (BCC) were observed by a multispectral laser-based device with a built-in neural network (NN) “DSL-1”. We measured the fluorescence spectra of suspected non-melanoma skin cancers and compared them with “normal” skin spectra. These spectra were input into DL algorithms to determine whether the skin is normal, pigmented normal, benign, or BCC. The preoperative differential AI-driven fluorescence diagnosis method correctly predicted the BCC lesions. We obtained an average sensitivity of 62% and average specificity of 83% in our experiments. Thus, the presented “DSL-1” diagnostic device can be a viable tool for the real-time diagnosis and guidance of non-melanoma skin cancer resection.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.

Future artificial intelligence tools and perspectives in medicine

PURPOSE OF REVIEW

Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine-learning methods to measure the model performance. In this review, we summarized and discussed the most recent radiomic pipeline used for clinical analysis.

RECENT FINDINGS

Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. Most artificial intelligence tools are based on imaging features, known as radiomic analysis that can be refined into predictive models in noninvasively acquired imaging data. This review explores the progress of artificial intelligence-based radiomic tools for clinical applications with a brief description of necessary technical steps. Explaining new radiomic approaches based on deep-learning techniques will explain how the new radiomic models (deep radiomic analysis) can benefit from deep convolutional neural networks and be applied on limited data sets.

SUMMARY

To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.

Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

End-to-end face parsing via interlinked convolutional neural networks

Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model for face parsing. However, the original iCNN was trained separately in two stages, limiting its performance. To solve this problem, we introduce a simple, end-to-end face parsing framework: STN-aided iCNN(STN-iCNN), which extends the iCNN by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to these two advantages, our approach significantly improves the accuracy of the original model. Our model achieved competitive performance on the Helen Dataset, the standard face parsing dataset. It also achieved superior performance on CelebAMask-HQ dataset, proving its good generalization. Our code has been released at https://github.com/aod321/STN-iCNN.

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.

Agreement of two pre-trained deep-learning neural networks built with transfer learning with six pathologists on 6000 patches of prostate cancer from Gleason2019 Challenge

INTRODUCTION

While the visual inspection of histopathology images by expert pathologists remains the golden standard method for grading of prostate cancer the quest for developing automated algorithms for the job is set and deep-learning techniques have emerged on top of other approaches.

METHODS

Two pre-trained deep-learning networks, obtained with transfer learning from two general purpose classification networks - AlexNet and GoogleNet, originally trained on a proprietary dataset of prostate cancer were used to classify 6000 cropped images from Gleason2019 Challenge.

RESULTS

The average agreement between the two networks and the six pathologists was found to be substantial for AlexNet and moderate for GoogleNet. When tested against the majority vote of the six pathologists the agreement was perfect and moderate for AlexNet, and GoogleNet, respectively. Despite our expectations, the average inter-pathologist agreement was moderate, while between the two networks it was substantial. Resulted accuracy for AlexNet and GoogleNet when tested against the majority vote as ground truth was of 85.51% and 74.75%, respectively. This result was higher than the score obtained on the dataset that they were trained on, showing their generalization capabilities.

CONCLUSIONS

Both the agreement and the accuracy indicate a better performance of AlexNet over GoogleNet, making it suitable for clinical deployment thus could potentially contribute to faster, more accurate and with higher reproducibility prostate cancer diagnosis.