Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.01.09.523193v1?rss=1
Authors: Jose, A., Roy, R., Stegmaier, J.
Abstract:
Training deep-learning models for biomedical images has always been a problem because of the unavailability of annotated training data. Here we propose using a model and a training approach for the weakly-supervised temporal classification of cell-cycle stages during mitosis. Instead of using annotated data, by using an ordered set of classes called transcripts, our proposed approach classifies the cell-cycle stages of cell video sequences. The network design helps to propagate information in time using Recurrent Neural Network layers and helps to focus the features on the center-cell using two loss functions. The proposed system is evaluated on four datasets from LiveCellMiner and has a comparable performance with the supervised approaches, considering the annotated data is not used for training. The predicted classification of the unannotated interphase-recovery class by our approach also aligns with the findings from the LiveCellMiner.
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