Saliency mapping by feature reduction and perturbation modeling in medical imaging
US11263744B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 9, 2019 |
| Grant date | Mar 1, 2022 |
| Priority date | — |
| Expiry date | Mar 10, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2211/421
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
For saliency mapping, a machine-learned classifier is used to classify input data. A perturbation encoder is trained and/or applied for saliency mapping of the machine-learned classifier. The training and/or application (testing) of the perturbation encoder uses less than all feature maps of the machine-learned classifier, such as selecting different feature maps of different hidden layers in a multiscale approach. The subset used is selected based on gradients from back-projection. The training of the perturbation encoder may be unsupervised, such as using an entropy score, or semi-supervised, such as using the entropy score and a difference of a perturbation mask from a ground truth segmentation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.