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arXiv · 2209.07819

Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels

Jan Oscar Cross-Zamirski·Guy Williams·Elizabeth Mouchet·Carola-Bibiane Schönlieb·Riku Turkki·Yinhai Wang
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Abstract

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%), and not-same-compound-and-batch performance (96%) using the compound as the weak label. Our method bypasses 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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Jan Oscar Cross-Zamirski, Guy Williams, Elizabeth Mouchet, Carola-Bibiane Schönlieb, Riku Turkki, Yinhai Wang. 2022-09-16. Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels. https://arxiv.org/abs/2209.07819

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