Abstract: The present disclosure relates to phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving a target image of a target biological cell having a target phenotype. The method also includes obtaining a semantic embedding associated with the target image. The semantic embedding is generated using a machine-learned, deep metric network model. Further, the method includes obtaining, for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image. In addition, the method includes identifying, for each of the semantic embeddings, common morphological variations and reducing, for each of the semantic embeddings based on the identified common morphological variations, effects of nuisances. Even further, the method includes determining, by the computing device, a similarity score for each candidate image.

Abstract: The present disclosure relates to analysis of perturbed subjects using semantic embeddings. One example embodiment includes a method. The method includes applying a respective perturbation to each of a plurality of subjects in a controlled environment. The method also includes producing a respective visual representation for each of the perturbed subjects using at least one imaging modality. Further, the method includes obtaining, by a computing device for each of the respective visual representations, a corresponding semantic embedding associated with the respective visual representation. The semantic embedding associated with the respective visual representation is generated using a machine-learned, deep metric network model. In addition, the method includes classifying, by the computing device based on the corresponding semantic embedding, each of the visual representations into one or more groups.

Abstract: The present disclosure relates to a phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving, by a computing device, a plurality of candidate images of candidate biological cells each having a respective candidate phenotype. The method also includes obtaining, by the computing device for each of the plurality of candidate images, a semantic embedding associated with the respective candidate image. Further, the method includes grouping, by the computing device, the plurality of candidate images into a plurality of phenotypic strata based on their respective semantic embeddings.

Abstract: The disclosure relates to phenotype analysis of cellular image data using a machine-learned, deep metric network model. An example method includes receiving, by a computing device, a target image of a target biological cell having a target phenotype. Further, the method includes obtaining, by the computing device, semantic embeddings associated with the target image and each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype. The semantic embeddings are generated using a machine-learned, deep metric network model. In addition, the method includes determining, by the computing device, a similarity score for each candidate image. Determining the similarity score for a respective candidate image includes computing a vector distance between the respective candidate image and the target image. The similarity score for each candidate image represents a degree of similarity between the target phenotype and the respective candidate phenotype.