{Reference Type}: Journal Article
{Title}: Differentiable optimization layers enhance GNN-based mitosis detection.
{Author}: Zhang H;Nguyen DH;Tsuda K;
{Journal}: Sci Rep
{Volume}: 13
{Issue}: 1
{Year}: 2023 08 31
{Factor}: 4.996
{DOI}: 10.1038/s41598-023-41562-y
{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.