Multi-modal Transformer for Brain Tumor Segmentation

Author:

Cho JihoonORCID,Park JinahORCID

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

2. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

3. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1–13 (2017)

4. Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

5. Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Review of MRI brain tumor segmentation and MGMT promoter classification methods on BraTs dataset based on Deep learning;2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP);2024-07-11

2. Disentangled multimodal brain MR image translation via transformer-based modality infuser;Medical Imaging 2024: Image Processing;2024-04-02

3. Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images;IEEE Transactions on Image Processing;2024

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