Weakly supervised learning with whole slide images
US11954596B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Mar 10, 2020 |
| Grant date | Apr 9, 2024 |
| Priority date | — |
| Expiry date | Feb 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.