Skin lesion segmentation using deep convolution networks guided by local unsupervised learning
US10223788B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 24, 2017 |
| Grant date | Mar 5, 2019 |
| Priority date | — |
| Expiry date | Apr 5, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30096
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A dermoscopic lesion area is identified by: Obtaining a dermoscopic image and running a convolutional neural network image classifier on the dermoscopic image to obtain pixelwise lesion prediction scores. Segmenting the dermoscopic image into super-pixels, and computing for each super-pixel an average of the pixelwise prediction scores for pixels within that super-pixel. Computing a mean prediction score across the plurality of super-pixels. Assigning a confidence indicator of “1” to each super-pixel with a prediction score equal or greater than the mean prediction score, and a confidence indicator of “0” to each super-pixel with a prediction score less than the mean prediction score. Constructing a super-pixel graph G=(V,E,W) wherein and di=Σi=1Nwij; computing a confidence score function F according to {circumflex over (F)}=arg min(FTLF+μ∥F−Y∥2); and integrating the confidence score function F with the pixelwise prediction scores to produce a final segmentation of the dermoscopic image into lesion and background areas.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.