Skin lesion segmentation using deep convolution networks guided by local unsupervised learning

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 w ij = exp ( -  x i - x j  2 σ )   and   d i = ∑ i = 1 N  w ij ; 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.

Dependencies are inferred from the claim text.