Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
| Autor: | Kraus, Oren, Kenyon-Dean, Kian, Saberian, Saber, Fallah, Maryam, McLean, Peter, Leung, Jess, Sharma, Vasudev, Khan, Ayla, Balakrishnan, Jia, Celik, Safiye, Beaini, Dominique, Sypetkowski, Maciej, Cheng, Chi Vicky, Morse, Kristen, Makes, Maureen, Mabey, Ben, Earnshaw, Berton |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: CVPR 2024 Highlight. arXiv admin note: text overlap with arXiv:2309.16064 |
| Databáze: | arXiv |
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