7 papers · 1 filter
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Yunzhou Li, Jiesi Hu, Yanwu Yang +5
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and…
Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactiv…
Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
Jiesi Hu, Yanwu Yang, Zhiyu Ye +4
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating t…
Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement
Jiesi Hu, Jianfeng Cao, Yanwu Yang +4
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, c…
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
Yanwu Yang, Guinan Su, Jiesi Hu +3
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range…
Advancing Brain Imaging Analysis Step-by-step via Progressive Self-paced Learning
Yanwu Yang, Hairui Chen, Jiesi Hu +2
Recent advancements in deep learning have shifted the development of brain imaging analysis. However, several challenges remain, such as heterogeneity, individual variations, and t…
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Jiesi Hu, Yanwu Yang, Xutao Guo +2
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging sce…