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 • 30 Nov 2023 • Ziyang Chen, Yiwen Ye, Mengkang Lu, Yongsheng Pan, 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 • 29 Nov 2023 • 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.