Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques
US10430946B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 14, 2019 |
| Grant date | Oct 1, 2019 |
| Priority date | — |
| Expiry date | Mar 14, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to improved techniques for performing computer vision functions on medical images, including object segmentation functions for identifying medical objects in the medical images and grading functions for determining severity labels for medical conditions exhibited in the medical images. The techniques described herein utilize a neural network architecture to perform these and other functions. The neural network architecture can be trained, at least in part, using semi-supervised learning techniques that enable the neural network architecture to accurately perform the object segmentation and grading functions despite limited availability of pixel-level annotation information.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.