Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data. (arXiv:2203.04289v1 [q-bio.QM])

In this work, we propose two novel methodologies to study temporal and
morphological phenotypic effects caused by different experimental conditions
using imaging data. As a proof of concept, we apply them to analyze drug
effects in 2D cancer cell cultures. We train a convolutional autoencoder on 1M
images dataset with random augmentations and multi-crops to use as feature
extractor. We systematically compare it to the pretrained state-of-the-art
models. We further use the feature extractor in two ways. First, we apply
distance-based analysis and dynamic time warping to cluster temporal patterns
of 31 drugs. We identify clusters allowing annotation of drugs as having
cytotoxic, cytostatic, mixed or no effect. Second, we implement an
adversarial/regularized learning setup to improve classification of 31 drugs
and visualize image regions that contribute to the improvement. We increase
top-3 classification accuracy by 8% on average and mine examples of
morphological feature importance maps. We provide the feature extractor and the
weights to foster transfer learning applications in biology. We also discuss
utility of other pretrained models and applicability of our methods to other
types of biomedical data.



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