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Abstract
Breast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature on deep learning based on input modalities, use of labeled/unlabeled data during training, and different architectures. Additionally, it describes more complex frameworks structured using hierarchical, ensemble, or fused learning. Then, MRI processing approaches, key aspects of convolutional neural networks, and key gaps and challenges are focused. The need for large breast MRI datasets with accurate annotations and the generalization of the proposed structures to diverse and comprehensive datasets are among the gaps.
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/ACCESS.2023.3321272
- License
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
IEEE Access
no. 11,
pages 117935 - 117946,
ISSN: 2169-3536 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Askaripour K., Żak A.: Breast MRI segmentation by deep learning: key gaps and challenges// IEEE Access -Vol. 11, (2023), s.117935-117946
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/access.2023.3321272
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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