1 code implementation • 20 Nov 2023 • Jin Ye, Junlong Cheng, Jianpin Chen, Zhongying Deng, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Min Zhu, Shaoting Zhang, Junjun He, Yu Qiao
Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes.
1 code implementation • 23 Oct 2023 • Haoyu Wang, Sizheng Guo, Jin Ye, Zhongying Deng, Junlong Cheng, Tianbin Li, Jianpin Chen, Yanzhou Su, Ziyan Huang, Yiqing Shen, Bin Fu, Shaoting Zhang, Junjun He, Yu Qiao
These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.
2 code implementations • 7 Sep 2023 • Ziyan Huang, Zhongying Deng, Jin Ye, Haoyu Wang, Yanzhou Su, Tianbin Li, Hui Sun, Junlong Cheng, Jianpin Chen, Junjun He, Yun Gu, Shaoting Zhang, Lixu Gu, Yu Qiao
To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation.
3 code implementations • 30 Aug 2023 • Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Junjun He, Shaoting Zhang, Min Zhu, Yu Qiao
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
no code implementations • 6 Jul 2023 • Junlong Cheng, Chengrui Gao, Fengjie Wang, Min Zhu
Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure.
no code implementations • 27 Oct 2021 • Junlong Cheng, Chengrui Gao, Hongchun Lu, Zhangqiang Ming, Yong Yang, Min Zhu
In recent years, segmentation methods based on deep convolutional neural networks (CNNs) have made state-of-the-art achievements for many medical analysis tasks.
no code implementations • 10 Oct 2021 • Zhangqiang Ming, Min Zhu, Xiangkun Wang, Jiamin Zhu, Junlong Cheng, Chengrui Gao, Yong Yang, XiaoYong Wei
In recent years, with the increasing demand for public safety and the rapid development of intelligent surveillance networks, person re-identification (Re-ID) has become one of the hot research topics in the computer vision field.