MamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913570677063680 |
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| author | Ibragimov, Alisher Senotrusova, Sofya Litvinov, Arsenii Ushakov, Egor Karpulevich, Evgeny Markin, Yury |
| author_facet | Ibragimov, Alisher Senotrusova, Sofya Litvinov, Arsenii Ushakov, Egor Karpulevich, Evgeny Markin, Yury |
| contents | In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three additional images: another view of the same breast and two images of the other breast. This approach enables the algorithm to closely replicate the practice of a radiologist who reviews the entire set of mammograms for a patient. Furthermore, this paper emphasizes the preprocessing of images, specifically proposing a cropping model (U-Net based on ResNet-34) to help the method remove image artifacts and focus on the breast region. To the best of our knowledge, this study is the first to achieve a ROC-AUC of 84.0 $\pm$ 1.7 and an F1 score of 56.0 $\pm$ 1.3 on an independent test dataset of Vietnam digital mammography (VinDr-Mammo), which is preprocessed with the cropping model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01669 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification Ibragimov, Alisher Senotrusova, Sofya Litvinov, Arsenii Ushakov, Egor Karpulevich, Evgeny Markin, Yury Image and Video Processing Computer Vision and Pattern Recognition In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three additional images: another view of the same breast and two images of the other breast. This approach enables the algorithm to closely replicate the practice of a radiologist who reviews the entire set of mammograms for a patient. Furthermore, this paper emphasizes the preprocessing of images, specifically proposing a cropping model (U-Net based on ResNet-34) to help the method remove image artifacts and focus on the breast region. To the best of our knowledge, this study is the first to achieve a ROC-AUC of 84.0 $\pm$ 1.7 and an F1 score of 56.0 $\pm$ 1.3 on an independent test dataset of Vietnam digital mammography (VinDr-Mammo), which is preprocessed with the cropping model. |
| title | MamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.01669 |