As a popular probabilistic generative model, generative adversarial network (GAN) has
been successfully used not only in natural image processing, but also in medical image …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments
during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality …

Integrating Stereo Imaging technology into medical diagnostics and surgeries marks a
significant revolution in medical sciences. This advancement gives surgeons and physicians …

In the last decade, numerous supervised deep learning approaches have been proposed for
visual–inertial odometry (VIO) and depth map estimation, which require large amounts of …

Current capsule endoscopes and next-generation robotic capsules for diagnosis and
treatment of gastrointestinal diseases are complex cyber-physical platforms that must …

This article reviews deep learning applications in biomedical optics with a particular
emphasis on image formation. The review is organized by imaging domains within …

This study aimed to evaluate a deep learning model for generating synthetic contrast-
enhanced CT (sCECT) from non-contrast chest CT (NCCT). A deep learning model was …

Objective To assess automatic computer‐aided in situ recognition of the morphological
features of pure and mixed urinary stones using intra‐operative digital endoscopic images …

High-resolution medical images are of critical significance to improve disease diagnosis.
Limited by the camera and power of medical devices, medical images often have very low …