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Review of Breast Cancer Pathologigcal Image Processing
Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478535/ https://www.ncbi.nlm.nih.gov/pubmed/34595234 http://dx.doi.org/10.1155/2021/1994764 |
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author | Zhang, Ya-nan XIA, Ke-rui LI, Chang-yi WEI, Ben-li Zhang, Bing |
author_facet | Zhang, Ya-nan XIA, Ke-rui LI, Chang-yi WEI, Ben-li Zhang, Bing |
author_sort | Zhang, Ya-nan |
collection | PubMed |
description | Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion. The achievements and application scope of supervised learning, unsupervised learning, deep learning, CNN, and so on in breast cancer examination are expounded. The prospect of unsupervised learning and transfer learning for breast cancer diagnosis is prospected. Finally, the privacy protection of breast cancer patients is put forward. |
format | Online Article Text |
id | pubmed-8478535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84785352021-09-29 Review of Breast Cancer Pathologigcal Image Processing Zhang, Ya-nan XIA, Ke-rui LI, Chang-yi WEI, Ben-li Zhang, Bing Biomed Res Int Review Article Breast cancer is one of the most common malignancies. Pathological image processing of breast has become an important means for early diagnosis of breast cancer. Using medical image processing to assist doctors to detect potential breast cancer as early as possible has always been a hot topic in the field of medical image diagnosis. In this paper, a breast cancer recognition method based on image processing is systematically expounded from four aspects: breast cancer detection, image segmentation, image registration, and image fusion. The achievements and application scope of supervised learning, unsupervised learning, deep learning, CNN, and so on in breast cancer examination are expounded. The prospect of unsupervised learning and transfer learning for breast cancer diagnosis is prospected. Finally, the privacy protection of breast cancer patients is put forward. Hindawi 2021-09-20 /pmc/articles/PMC8478535/ /pubmed/34595234 http://dx.doi.org/10.1155/2021/1994764 Text en Copyright © 2021 Ya-nan Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Zhang, Ya-nan XIA, Ke-rui LI, Chang-yi WEI, Ben-li Zhang, Bing Review of Breast Cancer Pathologigcal Image Processing |
title | Review of Breast Cancer Pathologigcal Image Processing |
title_full | Review of Breast Cancer Pathologigcal Image Processing |
title_fullStr | Review of Breast Cancer Pathologigcal Image Processing |
title_full_unstemmed | Review of Breast Cancer Pathologigcal Image Processing |
title_short | Review of Breast Cancer Pathologigcal Image Processing |
title_sort | review of breast cancer pathologigcal image processing |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478535/ https://www.ncbi.nlm.nih.gov/pubmed/34595234 http://dx.doi.org/10.1155/2021/1994764 |
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