Weakly and fully labeled mammogram classification and localization with a dual branch deep neural network
US10789462B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 15, 2019 |
| Grant date | Sep 29, 2020 |
| Priority date | — |
| Expiry date | Mar 30, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments may classify medical images, such as mammograms, using weakly labeled data sets, fully labeled data sets, or a combination of both. For example, a method may comprise receiving a whole medical image, extracting a plurality of image patches from the whole medical image, each image patch including a portion of the whole image, generating a representation of features found in the plurality of image patches, classifying each image patch as including a malignant abnormality, a benign abnormality or not including an abnormality to form a classification for each patch, in parallel, the detection branch computes a malignant distribution over patches and a benign distribution over patches resulting in ranking of patches compare to one another for malignancy, and ranking of patches compare to one another for benign. Patches classification probabilities and ranking are multiplied and summed for malignant and benign, resulting in global malignant probability and global benign probability.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.