Deep reinforcement learning-enabled cryo-em data collection

A system for performing electron microscopy on a sample, the system comprising:

• one or more processors;
• and
• a deep-reinforcement learning platform including a trained sub-region quality assessment application, a feature extraction application, and trained Q-learning network;
• wherein the deep-reinforcement learning platform includes computing instructions configured to be executed by the one or more processors to:
• receive images of a grid structure comprising a plurality of sub-regions, wherein the images of the grid structure contain (i) a first subset of candidate sub-region images captured at a first magnification level and each of a different candidate sub-region and (ii) one or more group-level images captured at a second magnification level and containing a plurality of the different candidate sub-region;
• and
• provide the first subset of the images to the trained sub-region quality assessment application;
• wherein the trained sub-region quality assessment application includes computing instructions configured to be executed by the one or more processors to determine and output a quality score for each candidate sub-region;
• wherein the feature extraction application includes computing instructions configured to be executed by the one or more processors to:
• generate from the quality scores for each candidate sub-region image, group-level features for the group-level images;
• and
• apply the quality scores for each of the candidate sub-region images and the group-level extraction features to the trained Q-learning network;
• wherein the trained Q-learning network includes computing instructions configured to be executed by the one or more processors to determine Q-values for each candidate sub-region and identify a next sub-region amongst the candidate sub-regions.

A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to:

• receive, by one or more processors, images of a grid structure comprising a plurality of sub-regions, wherein the images of the grid structure contain (i) a first subset of candidate sub-region images captured at a first magnification level and each of a different candidate sub-region and (ii) one or more group-level images captured at a second magnification level and containing a plurality of the different candidate sub-region;
• provide, by the one or more processors, the first subset of the images to a trained sub-region quality assessment application and output, from the trained sub-region quality assessment application, a quality score for each candidate sub-region;
• generate, by the one or more processors, from the quality scores for each candidate sub-region image, group-level features for the group-level images, using a group-level feature extraction application;
• apply, by the one or more processors, the quality scores for each of the candidate sub-region images and the group-level extraction features to a trained Q-learning network, the trained Q-learning network determining Q-values for each candidate sub-region and identifying a next sub-region amongst the candidate sub-regions;
• and
• capture one or more a micrograph images of the next sub-region.