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- W4387319214 abstract "Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this paper, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020)." @default.
- W4387319214 created "2023-10-04" @default.
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- W4387319214 date "2023-01-01" @default.
- W4387319214 modified "2023-10-09" @default.
- W4387319214 title "WS-MTST: Weakly Supervised Multi-label Brain Tumor Segmentation with Transformers" @default.
- W4387319214 doi "https://doi.org/10.1109/jbhi.2023.3321602" @default.
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