Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
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Luyang Luo, Xi Wang, Yi Lin, Xiaoqi Ma, Andong Tan, Ronald Chan, Varut Vardhanabhuti, Winnie CW Chu, Kwang-Ting Cheng, Hao Chen
2023
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.
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