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arXiv · 2309.16064

Masked Autoencoders are Scalable Learners of Cellular Morphology

Oren Kraus·Kian Kenyon-Dean·Saber Saberian·Maryam Fallah·Peter McLean·Jess Leung·Vasudev Sharma·Ayla Khan·Jia Balakrishnan·Safiye Celik·Maciej Sypetkowski·Chi Vicky Cheng·Kristen Morse·Maureen Makes·Ben Mabey·Berton Earnshaw
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Abstract

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.

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Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah, Peter McLean, Jess Leung, Vasudev Sharma, Ayla Khan, Jia Balakrishnan, Safiye Celik, Maciej Sypetkowski, Chi Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw. 2023-09-27. Masked Autoencoders are Scalable Learners of Cellular Morphology. https://arxiv.org/abs/2309.16064

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