The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Background and objectives Optical coherence tomography (OCT) has ushered in a
transformative era in the domain of ophthalmology, offering non-invasive imaging with high …

Large numbers of histopathological images have been digitized into high resolution whole
slide images, opening opportunities in develo** computational image analysis tools to …

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks,
has made significant breakthroughs in medical imaging, particularly for image classification …

Automatic medical image analysis (eg, medical image classification) is widely used in the
early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable …

Background To reduce the high incidence and mortality of gastric cancer (GC), we aimed to
develop deep learning-based models to assist in predicting the diagnosis and overall …

Mistrust is a major barrier to implementing deep learning in healthcare settings. Entrustment
could be earned by conveying model certainty, or the probability that a given model output is …

Deep learning approaches have demonstrated remarkable progress in automatic Chest X-
ray analysis. The data-driven feature of deep models requires training data to cover a large …

The novel coronavirus disease 2019 (COVID-19) characterized by atypical pneumonia has
caused millions of deaths worldwide. Automatically segmenting lesions from chest …

Supervised deep learning has achieved prominent success in various diabetic macular
edema (DME) recognition tasks from optical coherence tomography (OCT) volumetric …