Predictive marker patterns in imaging data are a means to quantify disease and progression, but their identification is challenging, if the underlying biology is poorly understood. Here, we present a method to identify predictive texture patterns in medical images in an unsupervised way. Based on deep clustering networks, we simultaneously encode and cluster medical image patches in a low-dimensional latent space. The resulting clusters serve as features for disease staging, linking them to the underlying disease. We evaluate the method on 70 T1-weighted magnetic resonance images of patients with different stages of liver steatosis. The deep clustering approach is able to find predictive clusters with a stable ranking, differentiating between low and high steatosis with an F1-Score of 0.78.
@article{arxiv.2002.03721,
title = {Unsupervised deep clustering for predictive texture pattern discovery in medical images},
author = {Matthias Perkonigg and Daniel Sobotka and Ahmed Ba-Ssalamah and Georg Langs},
journal= {arXiv preprint arXiv:2002.03721},
year = {2020}
}