Summary of Vitally Consistent: Scaling Biological Representation Learning For Cell Microscopy, by Kian Kenyon-dean et al.
ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
by Kian Kenyon-Dean, Zitong Jerry Wang, John Urbanik, Konstantin Donhauser, Jason Hartford, Saber Saberian, Nil Sahin, Ihab Bendidi, Safiye Celik, Marta Fay, Juan Sebastian Rodriguez Vera, Imran S Haque, Oren Kraus
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Medium Difficulty Summary: This research presents the largest foundation model to date for cell microscopy data, a 1.9 billion-parameter ViT-G/8 MAE trained on over 8 billion image crops. The model achieves a 60% improvement in linear separability of genetic perturbations and outperforms previous models on whole-genome biological relationship recall and replicate consistency benchmarks. Key methods include training on a curated dataset and using biologically motivated linear probing tasks to optimize representation. Self-supervised vision transformers, pretrained on either natural or microscopy images, yield more biologically meaningful representations in intermediate blocks than final blocks. This work provides insights for building foundation models for large-scale biological data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Scientists are trying to understand how cells react to different chemicals and genetic changes. To do this, they need special computer models that can take thousands of images of cells and turn them into a code that shows what’s happening inside the cell. The authors of this paper created a new model called ViT-G/8 MAE that is really good at doing this. It’s trained on millions of images and does better than previous models in predicting how different chemicals affect cells. They also found that some special computer models can learn to recognize patterns in these images that are helpful for understanding cell biology. |
Keywords
» Artificial intelligence » Mae » Recall » Self supervised » Vit
