mlsanityData to 7 Oct 2026

MamT4: Multi-view Attention Networks for Mammography Cancer Classification

arXiv 2411.01669 ↗First posted 3 Nov 2024eess.IV cs.CVcited by 1

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Abstract (arXiv)

In this study, we introduce a novel method, called MamT4, 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 ± 1.7 and an F1 score of 56.0 ± 1.3 on an independent test dataset of Vietnam digital mammography (VinDr-Mammo), which is preprocessed with the cropping model.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

18 of 46 references.

References · 18

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6 more
  1. 2017End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Designcited by 87
  2. 2022VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammographycited by 83
  3. 2017High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networkscited by 25
  4. 2021A novel multi-view deep learning approach for BI-RADS and density assessment of mammogramscited by 15
  5. 2022Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisationcited by 12
  6. 2023Delving into Ipsilateral Mammogram Assessment under Multi-View Networkcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammographycited by 0