Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

Q: What does this research mean for the field?

Swin Transformer models outperform CNN-based ResNet models in predicting IDH mutation status in gliomas, and using bounding box input images enables accurate prediction without the need for refined tumor segmentation. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jiangfen Wu; Qian Xu; Yiqing Shen; Weidao Chen; Kai Xu; Xian Qi

doi:10.3390/jcm11154625

August 8, 2022Journal of Clinical MedicineOpen Access

Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

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Abstract

Swin Transformer outperforms the CNN-based ResNet in IDH prediction. Using bounding box input images benefits the DL networks in IDH prediction and makes the IDH prediction free of refined glioma segmentation feasible.

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Wu et al. (2022) studied this question.

synapsesocial.com/papers/6a0830e3113ba5b476de0407 https://doi.org/10.3390/jcm11154625

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Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

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Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation

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