Multi-task deep-learning method and system to classify diabetic macular edema for different optical coherence tomography devices
US12361697B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 26, 2022 |
| Grant date | Jul 15, 2025 |
| Priority date | — |
| Expiry date | Sep 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30041
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A deep-learning method and systems for analyzing optical coherence tomography (OCT) images based on a convolutional neural network are provided. The method includes extracting a feature from one or more three-dimensional OCT volumetric scan images and classifying the OCT images with respect to diabetic macular edema (DME) based on results the feature extracted. The step of extracting a feature from one or more three-dimensional OCT volumetric scan images is performed by a neural network such as a neural network based on a ResNet-34 architecture. The method can further include extracting a feature from one or more two-dimensional (2D) OCT B-scan images and classifying the OCT images with respect to DME based on results of the 2D feature extracted. The step of extracting a feature from one or more 2D OCT B-scan images is performed by a neural network such as a neural network based on a ResNet-18 architecture.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.