US 12,119,090 B1 · Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

Abstract

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).

Claims20

Dependent claims

A computer-implemented method comprising:

generating a masked training phenomic image by applying a mask to a training phenomic image portraying a cell phenotype;
and
training a generative machine learning model to generate phenomic perturbation image embeddings from the training phenomic image by:
generating, utilizing the generative machine learning model, a predicted phenomic image from the masked training phenomic image;
generating a Fourier transformation loss between the predicted phenomic image and the training phenomic image;
and
modifying parameters of the generative machine learning model utilizing the Fourier transformation loss and a first Fourier weight.

4 more elements

Dependencies are inferred from the claim text.

Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

Life of this patent

Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings

Life of this patent

Abstract

Claims20