1 code implementation • 17 Mar 2024 • Shumeng Li, Lei Qi, Qian Yu, Jing Huo, Yinghuan Shi, Yang Gao
Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations.
1 code implementation • CVPR 2023 • Heng Cai, Shumeng Li, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao
Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Co-training (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks.
1 code implementation • IEEE Transactions on Medical Imaging 2022 • Shumeng Li, Heng Cai; Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao
In this paper, by introducing an extremely sparse annotation way of labeling only one slice per 3D image, we investigate a novel barely-supervised segmentation setting with only a few sparsely-labeled images along with a large amount of unlabeled images.
1 code implementation • 23 Mar 2022 • Ziyuan Zhao, Kaixin Xu, Shumeng Li, Zeng Zeng, Cuntai Guan
Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce.
1 code implementation • 21 May 2021 • Shumeng Li, Ziyuan Zhao, Kaixin Xu, Zeng Zeng, Cuntai Guan
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.