Shift invariant loss for deep learning based image segmentation
US11200676B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 17, 2020 |
| Grant date | Dec 14, 2021 |
| Priority date | — |
| Expiry date | Jun 11, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20132
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.