Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

Data in real-world application often exhibit skewed class distribution which poses an intense
challenge for machine learning. Conventional classification algorithms are not effective in …

Surgical tool presence detection and surgical phase recognition are two fundamental yet
challenging tasks in surgical video analysis as well as very essential components in various …

Objective: Surgical activity recognition is a fundamental step in computer-assisted
interventions. This paper reviews the state-of-the-art in methods for automatic recognition of …

Artificial intelligence (AI) is gradually changing the practice of surgery with technological
advancements in imaging, navigation, and robotic intervention. In this article, we review the …

Importance When evaluating surgeons in the operating room, experienced physicians must
rely on live or recorded video to assess the surgeon's technical performance, an approach …