Automatic detection of mitosis using handcrafted and convolutional neural network features
US9430829B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 8, 2014 |
| Grant date | Aug 30, 2016 |
| Priority date | — |
| Expiry date | Dec 8, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30068
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
One example apparatus associated with detecting mitosis in breast cancer pathology images by combining handcrafted (HC) and convolutional neural network (CNN) features in a cascaded architecture includes a set of logics that acquires an image of a region of tissue, partitions the image into candidate patches, generates a first probability that the patch is mitotic using an HC feature set and a second probability that the patch is mitotic using a CNN-learned feature set, and classifies the patch based on the first probability and the second probability. If the first and second probabilities do not agree, the apparatus trains a cascaded classifier on the CNN-learned feature set and the HC feature set, generates a third probability that the patch is mitotic, and classifies the patch based on a weighted average of the first probability, the second probability, and the third probability.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.