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Saber Saberian

2 accepted papers

2025

ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy

ICML 2025poster

Deriving insights from experimentally generated datasets requires methods that can account for random and systematic measurement errors and remove them in order to accurately represent the underlying effects of the conditions being tested. Here we present a framework for pretraining on large-scale m…

Cited by 6SourcePDFScholar
2024

Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

CVPR 2024highlight

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 wit…