activity
20242026
most citedUnified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

InViC: Intent-aware Visual Cues for Medical Visual Question Answering

Zhisong Wang, Ziyang Chen, Zanting Ye +3

Medical visual question answering (Med-VQA) aims to answer clinically relevant questions grounded in medical images. However, existing multimodal large language models (MLLMs) ofte…

cs.CV20251 cited

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation

Linhao Li, Yiwen Ye, Ziyang Chen +1

3D medical image segmentation often faces heavy resource and time consumption, limiting its scalability and rapid deployment in clinical environments. Existing efficient segmentati…

cs.CV2025

MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation

Yiwen Ye, Yicheng Wu, Xiangde Luo +5

Foundation models have become a promising paradigm for advancing medical image analysis, particularly for segmentation tasks where downstream applications often emerge sequentially…

cs.CV2025

Enjoying Information Dividend: Gaze Track-based Medical Weakly Supervised Segmentation

Zhisong Wang, Yiwen Ye, Ziyang Chen +1

Weakly supervised semantic segmentation (WSSS) in medical imaging struggles with effectively using sparse annotations. One promising direction for WSSS leverages gaze annotations,…

cs.CV2024

Meta Curvature-Aware Minimization for Domain Generalization

Ziyang Chen, Yiwen Ye, Feilong Tang +2

Domain generalization (DG) aims to enhance the ability of models trained on source domains to generalize effectively to unseen domains. Recently, Sharpness-Aware Minimization (SAM)…

cs.CV2024

CoSAM: Self-Correcting SAM for Domain Generalization in 2D Medical Image Segmentation

Yihang Fu, Ziyang Chen, Yiwen Ye +3

Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to tra…