MamT${}^4$: Multi-view Attention Networks for Mammography Cancer Classification

Abstract

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.

arXiv commentThe crop model is available here: https://github.com/ispras/mammo_crop

References in corpus (5)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
• End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design
• Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography
• High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks