4 citations · 9 across the 19 of their papers we have counts for
7 papers · 1 filter
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Ming Hu, Kun Yuan, Yaling Shen +17
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challeng…
MONICA: Benchmarking on Long-tailed Medical Image Classification
Lie Ju, Siyuan Yan, Yukun Zhou +4
Long-tailed learning is considered to be an extremely challenging problem in data imbalance learning. It aims to train well-generalized models from a large number of images that fo…
A Multimodal Vision Foundation Model for Clinical Dermatology
Siyuan Yan, Zhen Yu, Clare Primiero +22
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep l…
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
Ming Hu, Peng Xia, Lin Wang +11
Surgical scene perception via videos is critical for advancing robotic surgery, telesurgery, and AI-assisted surgery, particularly in ophthalmology. However, the scarcity of divers…
Diffusion Model Driven Test-Time Image Adaptation for Robust Skin Lesion Classification
Ming Hu, Siyuan Yan, Peng Xia +5
Deep learning-based diagnostic systems have demonstrated potential in skin disease diagnosis. However, their performance can easily degrade on test domains due to distribution shif…
Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis
Siyuan Yan, Cheng Luo, Zhen Yu +1
Vision-language foundation models like CLIP have shown impressive zero-shot generalization, but finetuning on downstream datasets can cause overfitting and loss of its generalizati…
Prompt-driven Latent Domain Generalization for Medical Image Classification
Siyuan Yan, Chi Liu, Zhen Yu +7
Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifacts bias, camera variations, differences in the imaging station, etc.…