In this review, we explore the potential applications of Artificial General Intelligence (AGI)
models in healthcare, focusing on foundational Large Language Models (LLMs), Large …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Pulmonary Embolism (PE) represents a thrombus (“blood clot”), usually originating from a
lower extremity vein, that travels to the blood vessels in the lung, causing vascular …

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-
slide pathological images (WSIs) can largely benefit therapy decision and prognosis …

Human anatomy is the foundation of medical imaging and boasts one striking characteristic:
its hierarchy in nature, exhibiting two intrinsic properties:(1) locality: each anatomical …

The progressive depletion of the ozone layer poses a significant threat to both human health
and the environment. Prolonged exposure to ultraviolet radiation increases the risk of …

We have developed a United framework that integrates three self-supervised learning (SSL)
ingredients (discriminative, restorative, and adversarial learning), enabling collaborative …

Self-supervised learning (SSL) approaches have recently shown substantial success in
learning visual representations from unannotated images. Compared with photographic …

Background Deep learning models require large‐scale training to perform confidently, but
obtaining annotated datasets in medical imaging is challenging. Weak annotation has …

Background Weakly supervised learning promises reduced annotation effort while
maintaining performance. Purpose To compare weakly supervised training with full slice …