MamT4: Multi-View Attention Networks for Mammography Cancer Classification
Alisher Ibragimov, С.А. Сенотрусова, Arsenii Litvinov, Egor N. Ushakov, Evgeny Karpulevich, Yury Vitalievich Markin · 2024
In this study, we introduce a novel method, called$\text{MamT}^4$, 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.