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

TL;DR

This paper introduces MamT$^4$, a multi-view attention network for mammography cancer classification that closely mimics radiologist review by analyzing four images simultaneously, achieving state-of-the-art results on the VinDr-Mammo dataset.

Contribution

The study presents a novel multi-view attention network with a U-Net based cropping preprocessing, achieving high ROC-AUC and F1 scores on mammography data.

Findings

Achieved ROC-AUC of 84.0 on VinDr-Mammo dataset.

Introduced a U-Net based cropping model for preprocessing.

First to report these metrics on this dataset.

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.