%0 Journal Article
%T Differentiable optimization layers enhance GNN-based mitosis detection.
%A Zhang H
%A Nguyen DH
%A Tsuda K
%J Sci Rep
%V 13
%N 1
%D 2023 08 31
%M 37653108
%F 4.996
%R 10.1038/s41598-023-41562-y
%X 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.