Mammography with deep learning for breast cancer detection

TL;DR

This paper reviews how deep learning can improve X-ray mammography for more accurate breast cancer detection and screening.

Contribution

The paper reviews recent advances in deep learning for mammography and outlines challenges for clinical implementation.

Findings

Deep learning can enhance the accuracy of breast cancer detection in mammography.

Challenges include data privacy, model interpretability, and generalizability.

Future research should focus on refining algorithms for clinical integration.

Abstract

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

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