AI4CellFate: Interpretable Early Cell Fate Prediction with Generative AI
Cunha, I.; Panconi, L.; Bauer, S.; Gestin, M.; Latron, E.; Sahai, E.; Le Marois, A.; Griffie, J.
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Live-cell imaging provides a unique insight into complex cellular processes including single cell fate, but remains limited by both low-throughput and the lack of generalisable analytics for the multidimensional datasets it produces. This work introduces AI4CellFate, an interpretable and data-driven machine learning framework for predicting cell fate from microscopy timelapses, applied here to cancer therapy. By integrating generative AI and contrastive learning, AI4CellFate enables early fate prediction as well as visualisation of biologically relevant features, with limited annotation.
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