Unsupervised deep clustering for predictive texture pattern discovery in medical images
This work addresses the challenge of disease quantification in medical imaging for clinicians, but it is incremental as it applies existing deep clustering techniques to a specific domain.
The paper tackles the problem of identifying predictive texture patterns in medical images without prior biological knowledge, using an unsupervised deep clustering method that achieved an F1-Score of 0.78 in differentiating liver steatosis stages on a dataset of 70 MRI images.
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.