Self-supervised representation learning paradigm for medical images
US12211202B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 13, 2021 |
| Grant date | Jan 28, 2025 |
| Priority date | — |
| Expiry date | Nov 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described for learning feature representations of medical images using a self-supervised learning paradigm and employing those feature representations for automating downstream tasks such as image retrieval, image classification and other medical image processing tasks. According to an embodiment, computer-implemented method comprises generating alternate view images for respective medical images included in set of training images using one or more image augmentation techniques or one or more image selection techniques tailored based on domain knowledge associated with the respective medical images. The method further comprises training a transformer network to learn reference feature representations for the respective medical images using their alternate view images and a self-supervised training process. The method further comprises storing the reference feature representations in an indexed data structure with information identifying the respective medical images that correspond to the reference feature representations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.