Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

TL;DR

This paper reviews a decade of deep learning advancements in breast cancer imaging, highlighting progress, current challenges, and future research directions across various imaging modalities and clinical applications.

Contribution

It provides a comprehensive survey of deep learning methods applied to breast cancer imaging, summarizing past progress and outlining future challenges and opportunities.

Findings

Deep learning has significantly improved breast cancer image analysis.

Applications include screening, diagnosis, treatment response prediction, and prognosis.

Future research needs to address challenges like data scarcity and model interpretability.

Abstract

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. This paper provides an extensive review of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.