Enhanced convolutional neural network for image segmentation
US10140544B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 2, 2018 |
| Grant date | Nov 27, 2018 |
| Priority date | — |
| Expiry date | Apr 2, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30061
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to digital image segmentation and region of interest identification. A computer implemented image segmentation method and system are particularly disclosed, including a predictive model trained based on a deep fully convolutional neural network. The model is trained using a loss function in at least one intermediate layer in addition to a loss function at the final stage of the full convolutional neural network. The predictive segmentation model trained in such a manner requires less training parameters and facilitates quicker and more accurate identification of relevant local and global features in the input image. In one implementation, the fully convolutional neural network is further supplemented with a conditional adversarial neural networks iteratively trained with the fully convolutional neural network as a discriminator measuring the quality of the predictive model generated by the fully convolutional neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.