Barrett’s esophagus analysis using infinity Restricted Boltzmann Machines

Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions

Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

Convolutional Neural Networks for the evaluation of cancer in Barrett's esophagus: Explainable AI to lighten up the black-box

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of early-cancerous tissues in Barrett's esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts' previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts' delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model's sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts' insights, demonstrating how human knowledge may influence the correct computational learning.

Early neoplasia identification in Barrett's esophagus via attentive hierarchical aggregation and self-distillation

Automatic surveillance of early neoplasia in Barrett's esophagus (BE) is of great significance for improving the survival rate of esophageal cancer. It remains, however, a challenging task due to (1) the large variation of early neoplasia, (2) the existence of hard mimics, (3) the complicated anatomical and lighting environment in endoscopic images, and (4) the intrinsic real-time requirement of this application. We propose a novel end-to-end network equipped with an attentive hierarchical aggregation module and a self-distillation mechanism to comprehensively address these challenges. The hierarchical aggregation module is proposed to capture the complementariness of adjacent layers and hence strengthen the representation capability of each aggregated feature. Meanwhile, an attention mask is developed to selectively integrate the logits of each feature, which not only improves the prediction accuracy but also enhances the prediction interpretability. Furthermore, an efficient self-distillation mechanism is implemented based on a teacher-student architecture, where the student aims at capturing abstract high-level features while the teacher is applied to bring more low-level semantic details to calibrate the classification results. The proposed techniques are effective yet lightweight, improving the classification performance without sacrificing time performance, and thus achieving real-time inference. We extensively evaluate the proposed method on the MICCAI EndoVis Challenge Dataset. Experimental results demonstrate the proposed method can achieve competitive accuracy with a much faster speed than state-of-the-arts.

Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines

Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems.

Normalizing images is good to improve computer-assisted COVID-19 diagnosis

The Coronavirus Disease 2019 (COVID-19) outbreak, caused by the SARS-CoV-2 virus, surprised the whole world in an unprecedented and devastating way, resulting in almost 200,000 deaths and 2.3 million infections worldwide in less than 4 months. Moreover, the elevate capability of transmission threatens to collapse both the healthy and economic systems from most countries, stressing worse predictions for emerging countries. In such a turbulent scenario, fast diagnosis is essential for a successful treatment and isolation of patients, thus avoiding increasing the number of contaminations. However, traditional methods of detection using polymerase chain reaction are impractical in large scale due to elevate costs, material scarcity, and time demanded for processing. As an alternative, some researchers proposed a machine learning–based diagnosis considering chest X-ray analysis with promising results, thus opening room for possible improvements. This work introduces a different normalization approach that, together with an EfficientNet-B6-inspired neural network, can deal with COVID-19 diagnosis considering chest X-ray images. Experiments provided competitive results considering a lighter and faster architecture, thus fostering research toward COVID-19 detection.

Assisting Barrett's esophagus identification using endoscopic data augmentation based on Generative Adversarial Networks

Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.