Patent Document ID: 10134131
Application ID: 15433027

Base Claim:
1. A method, comprising: receiving, by a computing device, a target image of a target biological cell having a target phenotype; obtaining, by the computing device, a semantic embedding associated with the target image, wherein the semantic embedding associated with the target image is generated using a machine-learned, deep metric network model; obtaining, by the computing device 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, wherein the semantic embedding associated with the respective candidate image is generated using the machine-learned, deep metric network model; determining, by the computing device, a similarity score for each candidate image, wherein determining the similarity score for a respective candidate image comprises computing, by the computing device, a vector distance in a multi-dimensional space described by the semantic embeddings between the respective candidate image and the target image, and wherein the similarity score for each candidate image represents a degree of similarity between the target phenotype and the respective candidate phenotype; determining, by the computing device, a threshold similarity score; and determining, by the computing device, those candidate images having similarity scores that satisfy the threshold similarity score, wherein the target phenotype is a healthy phenotype, wherein the candidate images having similarity scores that satisfy the threshold similarity score have respective candidate phenotypes corresponding to the healthy phenotype, and wherein those candidate images having similarity scores that do not satisfy the threshold similarity score have respective candidate phenotypes corresponding to an unhealthy phenotype.

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Claim 17:
17. The method of claim 1 , wherein obtaining the semantic embedding associated with the target image or one of the respective candidate images comprises: retrieving, by the computing device, each channel of a respective image; obtaining, by the computing device, a semantic embedding for each channel of the respective image, wherein the semantic embedding for each channel of the respective image is generated using the machine-learned, deep metric network model; and concatenating, by the computing device, the semantic embeddings for each channel of the respective image into a single semantic embedding.