Skip to main content

Application Progress of Deep Learning in Imaging Examination of Breast Cancer

Publication ,  Journal Article
Wang, Y; Liu, J; Ma, J; Shao, R; Chen, T; Li, M
Published in: Journal of Frontiers of Computer Science and Technology
February 1, 2024

Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work.

Duke Scholars

Published In

Journal of Frontiers of Computer Science and Technology

DOI

ISSN

1673-9418

Publication Date

February 1, 2024

Volume

18

Issue

2

Start / End Page

301 / 319
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Wang, Y., Liu, J., Ma, J., Shao, R., Chen, T., & Li, M. (2024). Application Progress of Deep Learning in Imaging Examination of Breast Cancer. Journal of Frontiers of Computer Science and Technology, 18(2), 301–319. https://doi.org/10.3778/j.issn.1673-9418.2309033
Wang, Y., J. Liu, J. Ma, R. Shao, T. Chen, and M. Li. “Application Progress of Deep Learning in Imaging Examination of Breast Cancer.” Journal of Frontiers of Computer Science and Technology 18, no. 2 (February 1, 2024): 301–19. https://doi.org/10.3778/j.issn.1673-9418.2309033.
Wang Y, Liu J, Ma J, Shao R, Chen T, Li M. Application Progress of Deep Learning in Imaging Examination of Breast Cancer. Journal of Frontiers of Computer Science and Technology. 2024 Feb 1;18(2):301–19.
Wang, Y., et al. “Application Progress of Deep Learning in Imaging Examination of Breast Cancer.” Journal of Frontiers of Computer Science and Technology, vol. 18, no. 2, Feb. 2024, pp. 301–19. Scopus, doi:10.3778/j.issn.1673-9418.2309033.
Wang Y, Liu J, Ma J, Shao R, Chen T, Li M. Application Progress of Deep Learning in Imaging Examination of Breast Cancer. Journal of Frontiers of Computer Science and Technology. 2024 Feb 1;18(2):301–319.

Published In

Journal of Frontiers of Computer Science and Technology

DOI

ISSN

1673-9418

Publication Date

February 1, 2024

Volume

18

Issue

2

Start / End Page

301 / 319