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Article

Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning

School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
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Author to whom correspondence should be addressed.
Tomography 2024, 10(6), 848-868; https://doi.org/10.3390/tomography10060065
Submission received: 16 March 2024 / Revised: 15 May 2024 / Accepted: 20 May 2024 / Published: 1 June 2024
(This article belongs to the Topic AI in Medical Imaging and Image Processing)

Abstract

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.
Keywords: bilateral mammograms; deep learning; breast cancer; computer-aided diagnosis; interpretable classifier bilateral mammograms; deep learning; breast cancer; computer-aided diagnosis; interpretable classifier

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MDPI and ACS Style

Wen, X.; Li, J.; Yang, L. Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning. Tomography 2024, 10, 848-868. https://doi.org/10.3390/tomography10060065

AMA Style

Wen X, Li J, Yang L. Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning. Tomography. 2024; 10(6):848-868. https://doi.org/10.3390/tomography10060065

Chicago/Turabian Style

Wen, Xuesong, Jianjun Li, and Liyuan Yang. 2024. "Breast Cancer Diagnosis Method Based on Cross-Mammogram Four-View Interactive Learning" Tomography 10, no. 6: 848-868. https://doi.org/10.3390/tomography10060065

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