Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913833806725120 |
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| author | Hirata, Kodai Okita, Tsuyoshi |
| author_facet | Hirata, Kodai Okita, Tsuyoshi |
| contents | This paper proposes a method MTL-Swin-Unet which is multi-task learning using transformers for classification and semantic segmentation. For spurious-correlation problems, this method allows us to enhance the image representation with two other image representations: representation obtained by semantic segmentation and representation obtained by image reconstruction. In our experiments, the proposed method outperformed in F-value measure than other classifiers when the test data included slices from the same patient (no covariate shift). Similarly, when the test data did not include slices from the same patient (covariate shift setting), the proposed method outperformed in AUC measure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet Hirata, Kodai Okita, Tsuyoshi Machine Learning Computer Vision and Pattern Recognition This paper proposes a method MTL-Swin-Unet which is multi-task learning using transformers for classification and semantic segmentation. For spurious-correlation problems, this method allows us to enhance the image representation with two other image representations: representation obtained by semantic segmentation and representation obtained by image reconstruction. In our experiments, the proposed method outperformed in F-value measure than other classifiers when the test data included slices from the same patient (no covariate shift). Similarly, when the test data did not include slices from the same patient (covariate shift setting), the proposed method outperformed in AUC measure. |
| title | Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.06185 |