Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Generative models hold promise for revolutionizing medical education, robot-assisted
surgery, and data augmentation for machine learning. Despite progress in generating 2D …

Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery,
and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of diverse and …

Foundation models have exhibited remarkable success in various applications, such as
disease diagnosis and text report generation. To date, a foundation model for endoscopic …

Early detecting and removing cancerous colorectal polyps can effectively reduce the risk of
colorectal cancer. Computer intelligent segmentation techniques (CIST) can improve the …

Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …

Abstract Weakly supervised Video Anomaly Detection (wVAD) aims to distinguish anomalies
from normal events based on video-level supervision. Most existing works utilize Multiple …

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy)
images only, but during testing, they are able to classify normal and abnormal (or disease) …

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a
detector using a training set that contains only normal images. UAD approaches can be …

The classification of gigapixel histopathology images with deep multiple instance learning
models has become a critical task in digital pathology and precision medicine. In this work …