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Differentiable optimization layers enhance GNN-based mitosis detection
Automatic mitosis detection from video is an essential step in analyzing proliferative behaviour of cells. In existing studies, a conventional object detector such as Unet is combined with a link prediction algorithm to find correspondences between parent and daughter cells. However, they do not tak...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471751/ https://www.ncbi.nlm.nih.gov/pubmed/37653108 http://dx.doi.org/10.1038/s41598-023-41562-y |
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author | Zhang, Haishan Nguyen, Dai Hai Tsuda, Koji |
author_facet | Zhang, Haishan Nguyen, Dai Hai Tsuda, Koji |
author_sort | Zhang, Haishan |
collection | PubMed |
description | Automatic mitosis detection from video is an essential step in analyzing proliferative behaviour of cells. In existing studies, a conventional object detector such as Unet is combined with a link prediction algorithm to find correspondences between parent and daughter cells. However, they do not take into account the biological constraint that a cell in a frame can correspond to up to two cells in the next frame. Our model called GNN-DOL enables mitosis detection by complementing a graph neural network (GNN) with a differentiable optimization layer (DOL) that implements the constraint. In time-lapse microscopy sequences cultured under four different conditions, we observed that the layer substantially improved detection performance in comparison with GNN-based link prediction. Our results illustrate the importance of incorporating biological knowledge explicitly into deep learning models. |
format | Online Article Text |
id | pubmed-10471751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104717512023-09-02 Differentiable optimization layers enhance GNN-based mitosis detection Zhang, Haishan Nguyen, Dai Hai Tsuda, Koji Sci Rep Article Automatic mitosis detection from video is an essential step in analyzing proliferative behaviour of cells. In existing studies, a conventional object detector such as Unet is combined with a link prediction algorithm to find correspondences between parent and daughter cells. However, they do not take into account the biological constraint that a cell in a frame can correspond to up to two cells in the next frame. Our model called GNN-DOL enables mitosis detection by complementing a graph neural network (GNN) with a differentiable optimization layer (DOL) that implements the constraint. In time-lapse microscopy sequences cultured under four different conditions, we observed that the layer substantially improved detection performance in comparison with GNN-based link prediction. Our results illustrate the importance of incorporating biological knowledge explicitly into deep learning models. Nature Publishing Group UK 2023-08-31 /pmc/articles/PMC10471751/ /pubmed/37653108 http://dx.doi.org/10.1038/s41598-023-41562-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Haishan Nguyen, Dai Hai Tsuda, Koji Differentiable optimization layers enhance GNN-based mitosis detection |
title | Differentiable optimization layers enhance GNN-based mitosis detection |
title_full | Differentiable optimization layers enhance GNN-based mitosis detection |
title_fullStr | Differentiable optimization layers enhance GNN-based mitosis detection |
title_full_unstemmed | Differentiable optimization layers enhance GNN-based mitosis detection |
title_short | Differentiable optimization layers enhance GNN-based mitosis detection |
title_sort | differentiable optimization layers enhance gnn-based mitosis detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471751/ https://www.ncbi.nlm.nih.gov/pubmed/37653108 http://dx.doi.org/10.1038/s41598-023-41562-y |
work_keys_str_mv | AT zhanghaishan differentiableoptimizationlayersenhancegnnbasedmitosisdetection AT nguyendaihai differentiableoptimizationlayersenhancegnnbasedmitosisdetection AT tsudakoji differentiableoptimizationlayersenhancegnnbasedmitosisdetection |