Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology

1 code implementation • 16 Apr 2024 • 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

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images.

Drug Discovery Self-Supervised Learning

Masked Autoencoders are Scalable Learners of Cellular Morphology

1 code implementation • 27 Sep 2023 • 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

Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research.

RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods

no code implementations • 13 Jan 2023 • Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian, Oren Kraus, John Urbanik, James Taylor, Ben Mabey, Mason Victors, Jason Yosinski, Alborz Rezazadeh Sereshkeh, Imran Haque, Berton Earnshaw

We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task.