  Inferring biological relationships from cellular phenotypes in high-content
microscopy screens provides significant opportunity and challenge in biological
research. Prior results have shown that deep vision models can capture
biological signal better than hand-crafted features. This work explores how
self-supervised deep learning approaches scale when training larger models on
larger microscopy datasets. Our results show that both CNN- and ViT-based
masked autoencoders significantly outperform weakly supervised baselines. At
the high-end of our scale, a ViT-L/8 trained on over 3.5-billion unique crops
sampled from 93-million microscopy images achieves relative improvements as
high as 28% over our best weakly supervised baseline at inferring known
biological relationships curated from public databases. Relevant code and
select models released with this work can be found at:
https://github.com/recursionpharma/maes_microscopy.
