{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/masked-autoencoders-are-scalable-learners-of","title":"Masked Autoencoders are Scalable Learners of Cellular Morphology","arxiv_id":"2309.16064","date":"2023-09-27","proceeding":null,"authors":["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"],"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.","url_abs":"https://arxiv.org/abs/2309.16064v2","url_pdf":"https://arxiv.org/pdf/2309.16064v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"masked-autoencoders-are-scalable-learners-of","repo_url":"https://github.com/recursionpharma/maes_microscopy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.16064","atlas_url":"https://app.syntology.ai/?focus=2309.16064","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}