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Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

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

Arch 4A-E Poster #205
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Thu 20 Jun 10:30 a.m. PDT — noon PDT


Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datasets. Our results show that ViT-based MAEs outperform weakly supervised classifiers on a variety of tasks, achieving as much as a 11.5% relative improvement when recalling known biological relationships curated from public databases. Additionally, we develop a new channel-agnostic MAE architecture (CA-MAE) that allows for inputting images of different numbers and orders of channels at inference time. We demonstrate that CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset (JUMP-CP) generated under different experimental conditions with a different channel structure than our pretraining data (RPI-93M). Our findings motivate continued research into scaling self-supervised learning on microscopy data in order to create powerful foundation models of cellular biology that have the potential to catalyze advancements in drug discovery and beyond.

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