U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

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

Recent advances in learning multi-modal representation have witnessed the success in
biomedical domains. While established techniques enable handling multi-modal …

As a crucial and intricate task in robotic minimally invasive surgery, reconstructing surgical
scenes using stereo or monocular endoscopic video holds immense potential for clinical …

Tissue deformation poses a key challenge for accurate surgical scene reconstruction.
Despite yielding high reconstruction quality, existing methods suffer from slow rendering …

Generating diverse plausible outputs from a single input is crucial for addressing visual
ambiguities, exemplified in medical imaging where experts may provide varying semantic …

Reconstructing and understanding 3D structures from a limited number of images is a well-
established problem in computer vision. Traditional methods usually break this task into …

Tooth point cloud segmentation is a fundamental task in many orthodontic applications.
Current research mainly focuses on fully supervised learning which demands expensive …

Nasopharyngeal carcinoma (NPC) has high metastatic potential and is hard to detect early.
This study aims to develop a deep learning model for NPC diagnosis using optical imagery …

As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent
universality, they also present challenges in giving ambiguous and uncertain predictions …