Recursion Pharmaceuticals, Inc.

Dominique Beaini

Berton Allen Earnshaw

Peter Foster McLean

Safiye Celik

Mohammadsadegh Saberian

Maryam Fallah

Jaichitra Balakrishnan

Kian Runnels Kenyon-Dean

Oren Zeev Kraus

Maciej Sypetkowski

Ayla Yasmin Khan

Chi Cheng

Benjamin John Mabey

Jessica Wai Yin Leung

Kristen Rose Morse

Maureen Katherine Makes

Vasudev Sharma

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing generative machine learning models to generate embeddings from phenomic images (or other microscopy representations). For example, the disclosed systems can train a generative machine learning model (e.g., a masked autoencoder generative model) to generate predicted (or reconstructed) phenomic images from masked version of ground truth training phenomic images. In some cases, the disclosed systems utilize a momentum-tracking optimizer while reducing a loss of the generative machine learning model to enable efficient training on large scale training image batches. Furthermore, the disclosed systems can utilize Fourier transformation losses with multi-stage weighting to improve the accuracy of the generative machine learning model on the phenomic images during training. Indeed, the disclosed systems can utilize the trained generative machine learning model to generate phenomic embeddings from input phenomic images (for various phenomic comparisons).

Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

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Montreal, Canada

Dominique Beaini