Phenotype analysis of cellular image data using a deep metric network
US10134131B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 15, 2017 |
| Grant date | Nov 20, 2018 |
| Priority date | — |
| Expiry date | Feb 15, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30024
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.