Unsupervised deep clustering for predictive texture pattern discovery in medical images

• URL: http://arxiv.org/abs/2002.03721v1
• Date: Fri, 31 Jan 2020 10:57:59 GMT
• Title: Unsupervised deep clustering for predictive texture pattern discovery in medical images
• Authors: Matthias Perkonigg and Daniel Sobotka and Ahmed Ba-Ssalamah and Georg Langs
• Abstract summary: 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. We evaluate the method on 70 T1-weighted magnetic resonance images of patients with different stages of liver steatosis.
• Score: 2.0478628221188497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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