Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autor principal: Lu, Wenjing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
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
_version_ 1784582653902585856
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