Characterizing pathology images with statistical analysis of local neural network responses
US9710695B2 · kind B2 · utility
13Cited by
3References
31Claims
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Key dates
| Filing date | Mar 14, 2014 |
| Grant date | Jul 18, 2017 |
| Priority date | — |
| Expiry date | Mar 14, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/764
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
For digital pathology imaging, intelligent processing, such as automatic recognition or content-based retrieval, is one significant benefit that drives the wide application of this technology. Before any intelligent processing on pathology images, every image is converted into a feature vector which quantitatively capture its visual characteristics. An algorithm characterizing pathology images with statistical analysis of local responses of neural networks is described herein. The algorithm framework enables extracting sophisticated textural features that are well adapted to the image data of interest.
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