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A Two-Phase Mitosis Detection Approach Based on U-Shaped Network
This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powe...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510810/ https://www.ncbi.nlm.nih.gov/pubmed/34651044 http://dx.doi.org/10.1155/2021/1722652 |
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author | Lu, Wenjing |
author_facet | Lu, Wenjing |
author_sort | Lu, Wenjing |
collection | PubMed |
description | This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powerful object detection methods hardly be used in mitosis detection. Aiming at solving this problem, this paper firstly applies a CNN-based algorithm to pixelwisely segment the mitosis regions, based on which bounding boxes of mitosis are generated as strong labels. Based on the generated bounding boxes, an object detection network is trained to accomplish mitosis detection. Experimental results show that the proposed method is effective in detecting mitosis, and the accuracies outperform state-of-the-art literatures. |
format | Online Article Text |
id | pubmed-8510810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-85108102021-10-13 A Two-Phase Mitosis Detection Approach Based on U-Shaped Network Lu, Wenjing Biomed Res Int Research Article This paper proposes a deep learning-based method for mitosis detection in breast histopathology images. A main problem in mitosis detection is that most of the datasets only have weak labels, i.e., only the coordinates indicating the center of the mitosis region. This makes most of the existing powerful object detection methods hardly be used in mitosis detection. Aiming at solving this problem, this paper firstly applies a CNN-based algorithm to pixelwisely segment the mitosis regions, based on which bounding boxes of mitosis are generated as strong labels. Based on the generated bounding boxes, an object detection network is trained to accomplish mitosis detection. Experimental results show that the proposed method is effective in detecting mitosis, and the accuracies outperform state-of-the-art literatures. Hindawi 2021-10-05 /pmc/articles/PMC8510810/ /pubmed/34651044 http://dx.doi.org/10.1155/2021/1722652 Text en Copyright © 2021 Wenjing Lu. 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 | Research Article Lu, Wenjing A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title | A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title_full | A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title_fullStr | A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title_full_unstemmed | A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title_short | A Two-Phase Mitosis Detection Approach Based on U-Shaped Network |
title_sort | two-phase mitosis detection approach based on u-shaped network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510810/ https://www.ncbi.nlm.nih.gov/pubmed/34651044 http://dx.doi.org/10.1155/2021/1722652 |
work_keys_str_mv | AT luwenjing atwophasemitosisdetectionapproachbasedonushapednetwork AT luwenjing twophasemitosisdetectionapproachbasedonushapednetwork |