no code implementations • 16 Dec 2024 • Ziyang Chen, Yiwen Ye, Feilong Tang, Yongsheng Pan, Yong Xia
However, SAM and its variants sometimes fail to guide the model toward a flat minimum, and their training processes exhibit limitations, hindering further improvements in model generalization.
no code implementations • 15 Nov 2024 • Yihang Fu, Ziyang Chen, Yiwen Ye, Xingliang Lei, Zhisong Wang, Yong Xia
Existing SAM-based approaches attempt to address the need for manual prompts by introducing prompt generators that automatically generate these prompts.
1 code implementation • 6 Nov 2024 • Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus H. Maier-Hein, Paul Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing, Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao, Haonan Wang, Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski, Saumya Gupta, Linshan Wu, Jiaxin Zhuang, Hao Chen, Holger Roth, Daguang Xu, Matthew B. Blaschko, Sergio Decherchi, Andrea Cavalli, Alan L. Yuille, Zongwei Zhou
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
no code implementations • 17 Oct 2024 • Ziyang Chen, Yiwen Ye, Yongsheng Pan, Jingfeng Zhang, Yanning Zhang, Yong Xia
To facilitate adaptation while preserving data privacy, source-free domain adaptation (SFDA) and test-time adaptation (TTA) have emerged as effective paradigms, relying solely on target domain data.
1 code implementation • 8 Oct 2024 • Yiwen Ye, Ziyang Chen, Jianpeng Zhang, Yutong Xie, Yong Xia
In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains.
no code implementations • 27 Aug 2024 • Xingliang Lei, Yiwen Ye, Ziyang Chen, Minglei Shu, Yong Xia
During this stage, the pre-trained backbone parameters are frozen, and only the target parameters are trainable.
no code implementations • 23 Aug 2024 • Zhisong Wang, Yiwen Ye, Ziyang Chen, Minglei Shu, Yong Xia
MaCo employs masked context modeling (MCM) and continuous pseudo labels (CPL).
1 code implementation • 14 Aug 2024 • Ziyang Chen, Yiwen Ye, Yongsheng Pan, Yong Xia
Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our GraTa method over other state-of-the-art TTA methods on a benchmark medical image segmentation task.
1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
1 code implementation • CVPR 2024 • Ziyang Chen, Yongsheng Pan, Yiwen Ye, Mengkang Lu, Yong Xia
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 in real-world applications.
1 code implementation • CVPR 2024 • Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Qi Wu, Yong Xia
In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.
1 code implementation • 31 May 2023 • Ziyang Chen, Yongsheng Pan, Yiwen Ye, Hengfei Cui, Yong Xia
In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution.
1 code implementation • 7 Apr 2023 • Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia
Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.
1 code implementation • 29 Aug 2022 • Shishuai Hu, Yiwen Ye, Zehui Liao, Yong Xia
Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings.