Deep Convolutional Neural Nets For Objective Steatosis Detection From Liver Samples

In present technical paper we describe an algorithm to detect steatosis. The golden standard in liver diagnosis is the biopsy. For clinical investigations the score given by a human expert is good enough. In developing noninvasive tools one needs objective and reproducible measurements of the biopsy parameters. There are two approaches proposed here, one based on classical computer vision and another one based on the deep convolutional neural nets. Tests on 100 patients clearly show that neural net approach is superior both in performance levels and in the amount of work that is invested. This paper can be included in the area of semantic segmentation but with recent advances in computer vision, the lines between segmentation and classification are blurred out.