Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

Jose, A.; Roy, R.; Stegmaier, J.

doi:10.1101/2023.01.09.523193

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bioRxiv · 10.1101/2023.01.09.523193

Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

Jose, A.·Roy, R.·Stegmaier, J.

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Abstract

Training deep-learning models for biomedical images has always been a problem due to the lack of annotated 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 transcript, 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 and helps to focus the features on the center-cell. The algorithm is evaluated on four datasets from LiveCellMiner and has a performance close to the supervised approaches, which is impressive, considering that annotated data is not used in training.

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Jose, A., Roy, R., Stegmaier, J.. 2023-01-09. Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks. https://doi.org/10.1101/2023.01.09.523193

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