Machine learning classification and training for digital microscopy cytology images
US10552663B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 2, 2018 |
| Grant date | Feb 4, 2020 |
| Priority date | — |
| Expiry date | Jul 25, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosure relates to machine learning classification of cells/particles in microscopy images. A method includes inputting an image having invisible features into an initial neural network classifier (INNC) of a convolutional neural network. The INNC is trained using images with ground truth derived from out-of-channel mechanisms. The method includes generating an intermediate classification from the original image. The intermediate classification and the original image are input into a final neural network classifier (FNNC) that comprises one or more bypass layers to feed forward an initial, final classification from a final activation layer to a final convolutional layer thereby bypassing a final pooling layer. The final convolutional layer has an increased kernel size and more filters than the initial convolutional layer. The final classification is generated based on the invisible features in the original image and outputted.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.