Unsupervised deep clustering for predictive texture pattern discovery in medical images Article Swipe

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Matthias Perkonigg , Daniel Sobotka , Ahmed Ba‐Ssalamah , Georg Langs ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2002.03721

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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Artificial Intelligence

Cluster Analysis

Computer Science

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Artificial intelligence Cluster analysis Pattern recognition (psychology) Computer science Texture (cosmology) Image (mathematics)

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2002.03721
PDF: https://arxiv.org/pdf/2002.03721
OA Status: green
Cited By: 2
References: 12
Related Works: 10
OpenAlex ID: https://openalex.org/W3005268706

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DOI: https://doi.org/10.48550/arxiv.2002.03721

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Title: Unsupervised deep clustering for predictive texture pattern discovery in medical images

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-01-31

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Authors: Matthias Perkonigg, Daniel Sobotka, Ahmed Ba‐Ssalamah, Georg Langs

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Landing page: https://arxiv.org/abs/2002.03721

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PDF URL: https://arxiv.org/pdf/2002.03721

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OA URL: https://arxiv.org/pdf/2002.03721

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Concepts: Artificial intelligence, Cluster analysis, Pattern recognition (psychology), Computer science, Texture (cosmology), Image (mathematics)

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Cited by: 2

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Citations by year (recent): 2023: 1, 2020: 1

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References (count): 12

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Related works (count): 10

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