10 citations · 13 across the 15 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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,…
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)…
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…
MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model
Yiwen Ye, Ziyang Chen, Jianpeng Zhang +2
Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities e…
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation
Ziyang Chen, Yiwen Ye, Yongsheng Pan +3
Distribution shifts widely exist in medical images acquired from different medical centres, hindering the deployment of semantic segmentation models trained on one centre (source d…
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training
Xingliang Lei, Yiwen Ye, Zhisong Wang +5
Parameter-efficient fine-tuning (PEFT) techniques have emerged to address overfitting and high computational costs associated with fully fine-tuning in self-supervised learning. Ma…
From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
Zhisong Wang, Yiwen Ye, Ziyang Chen +3
Scribble-based weakly supervised segmentation methods have shown promising results in medical image segmentation, significantly reducing annotation costs. However, existing approac…
Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation
Ziyang Chen, Yiwen Ye, Yongsheng Pan +1
Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders t…
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
Yiwen Ye, Yutong Xie, Jianpeng Zhang +3
Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consum…
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
Ziyang Chen, Yongsheng Pan, Yiwen Ye +2
Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model…