Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels
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We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells. Our model is based on a knowledge distillation approach with a vision transformer backbone (DINO), and we use this as a benchmark model for our study. Using WS-DINO, we fine-tuned with weak label information available in high-content microscopy screens (treatment and compound), and achieve state-of-the-art performance in not-same-compound mechanism of action prediction on the BBBC021 dataset (98 performance (96 single cell cropping as a pre-processing step, and using self-attention maps we show that the model learns structurally meaningful phenotypic profiles.
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