10 citations · 13 across the 15 of their papers we have counts for
15 papers
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,…
Test Time Training for 4D Medical Image Interpolation
Qikang Zhang, Yingjie Lei, Zihao Zheng +2
4D medical image interpolation is essential for improving temporal resolution and diagnostic precision in clinical applications. Previous works ignore the problem of distribution s…
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…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
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…