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

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 ca...

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Published inIEEE reviews in biomedical engineering Vol. 18; pp. 130 - 151
Main Authors Luo, Luyang, Wang, Xi, Lin, Yi, Ma, Xiaoqi, Tan, Andong, Chan, Ronald, Vardhanabhuti, Varut, Chu, Winnie CW, Cheng, Kwang-Ting, Chen, Hao
Format Journal Article
LanguageEnglish
Published United States IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Breast
Breast - diagnostic imaging
Breast cancer
Breast Neoplasms - diagnostic imaging
Deep Learning
Diagnosis
Digital imaging
Female
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging
Malignancy
Mammography
medical image analysis
Medical imaging
Pathology
Prognostics and health management
Ultrasonic imaging
Online AccessGet full text
ISSN1937-3333
1941-1189
1941-1189
DOI10.1109/RBME.2024.3357877

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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 mammograms, 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.
AbstractList 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 mammograms, 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.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 mammograms, 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.
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 mammograms, 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.
Author Luo, Luyang
Vardhanabhuti, Varut
Chan, Ronald
Cheng, Kwang-Ting
Chu, Winnie CW
Lin, Yi
Chen, Hao
Wang, Xi
Ma, Xiaoqi
Tan, Andong
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Snippet Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis...
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SubjectTerms Breast
Breast - diagnostic imaging
Breast cancer
Breast Neoplasms - diagnostic imaging
Deep Learning
Diagnosis
Digital imaging
Female
Humans
Image Interpretation, Computer-Assisted - methods
Magnetic Resonance Imaging
Malignancy
Mammography
medical image analysis
Medical imaging
Pathology
Prognostics and health management
Ultrasonic imaging
Title Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
URI https://ieeexplore.ieee.org/document/10413531
https://www.ncbi.nlm.nih.gov/pubmed/38265911
https://www.proquest.com/docview/3161363000
https://www.proquest.com/docview/2918511264
Volume 18
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