Phenotype analysis of cellular image data using a deep metric network
US11334770B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 3, 2020 |
| Grant date | May 17, 2022 |
| Priority date | — |
| Expiry date | Dec 17, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/10
- WIPO fieldMeasurement
- WIPO sectorInstruments
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.