Method of training massive training artificial neural networks (MTANN) for the detection of abnormalities in medical images
US6754380B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 14, 2003 |
| Grant date | Jun 22, 2004 |
| Priority date | — |
| Expiry date | Feb 14, 2023 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30004
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method, system, and computer program product of selecting a set of training images for a massive training artificial neural network (MTANN). The method comprises selecting the set of training images from a set of domain images; training the MTANN with the set of training images; applying a plurality of images from the set of domain images to the trained MTANN to obtain a corresponding plurality of scores; and determining the set of training images based on the plurality of images, the corresponding plurality of scores, and the set of training images. The method is useful for the reduction of false positives in computerized detection of abnormalities in medical images. In particular, the MTAAN can be used for the detection of lung nodules in low-dose CT (LDCT). The MTANN consists of a modified multilayer artificial neural network capable of operating on image data directly.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.