Summary of Masked Autoencoders For Microscopy Are Scalable Learners Of Cellular Biology, by Oren Kraus et al.
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
by 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 Vicky Cheng, Kristen Morse, Maureen Makes, Ben Mabey, Berton Earnshaw
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the scalability of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with larger model backbones and microscopy datasets. The results show that ViT-based MAEs outperform weakly supervised classifiers on various tasks, achieving a 11.5% relative improvement in recalling known biological relationships. A new channel-agnostic MAE architecture (CA-MAE) is developed to input images of different numbers and orders of channels at inference time. CA-MAEs effectively generalize by inferring and evaluating on a microscopy image dataset generated under different experimental conditions. The findings motivate continued research into scaling self-supervised learning on microscopy data for creating powerful foundation models of cellular biology, which can catalyze advancements in drug discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use big amounts of microscope images to help us understand biological processes better. It compares two types of AI models: weakly supervised classifiers and self-supervised masked autoencoders (MAEs). The MAEs do a much better job, especially when they’re trained on really large datasets. The paper also introduces a new way to make the MAEs work with images that have different channels or structures. This can help us generalize our findings to new situations. Overall, the results show that using self-supervised learning on microscope images can lead to big improvements in understanding biology and could even help develop new medicines. |
Keywords
» Artificial intelligence » Inference » Mae » Self supervised » Supervised » Vit
