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arXiv · 2002.03721

Unsupervised deep clustering for predictive texture pattern discovery in medical images

Matthias Perkonigg·Daniel Sobotka·Ahmed Ba-Ssalamah·Georg Langs
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Abstract

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.

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Matthias Perkonigg, Daniel Sobotka, Ahmed Ba-Ssalamah, Georg Langs. 2020-01-31. Unsupervised deep clustering for predictive texture pattern discovery in medical images. https://arxiv.org/abs/2002.03721

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