activity
20202026
most citedLeveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling

53 citations · 142 across the 29 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

eess.IV2024

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…

cs.CV2024★ 1 cited

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…

cs.CV2024

TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM

Wenxue Li, Xinyu Xiong, Peng Xia +2

Recent advances in large foundation models, such as the Segment Anything Model (SAM), have demonstrated considerable promise across various tasks. Despite their progress, these mod…

cs.CV2024

Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations

Peng Xia, Ming Hu, Feilong Tang +6

Diabetic Retinopathy (DR), induced by diabetes, poses a significant risk of visual impairment. Accurate and effective grading of DR aids in the treatment of this condition. Yet exi…

cs.CV2024★ 2 cited

Diversified and Personalized Multi-rater Medical Image Segmentation

Yicheng Wu, Xiangde Luo, Zhe Xu +5

Annotation ambiguity due to inherent data uncertainties such as blurred boundaries in medical scans and different observer expertise and preferences has become a major obstacle for…

eess.IV2024★ 2 cited

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.…