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

Keywords: Deep Learning, Representation Learning, Computer Vision, Self-Supervised Learning, Drug Discovery, Microscopy Imaging

TL;DR: WS-DINO: a novel framework to use weak label information in a self-supervised setting to learn phenotypic representations from high-content fluorescent images of cells.

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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/self-supervised-learning-of-phenotypic/code)