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Differentiable optimization layers enhance GNN-based mitosis detection.

ABSTRACT: 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.

SUBMITTER: Zhang H

PROVIDER: S-EPMC10471751 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Differentiable optimization layers enhance GNN-based mitosis detection.

Zhang Haishan H Nguyen Dai Hai DH Tsuda Koji K

Scientific reports 20230831 1

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 ...[more]

PMID: 37653108

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