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Deep learning excels in the analysis of unstructured data and recentadvancements allow to extend these techniques to survival analysis. In thecontext of clinical radiology, this enables, e.g., to relate unstructuredvolumetric images to a risk score or a prognosis of life expectancy and supportclinical decision making. Medical applications are, however, associated withhigh criticality and consequently, neither medical personnel nor patients dousually accept black box models as reason or basis for decisions. Apart fromaverseness to new technologies, this is due to missing interpretability,transparency and accountability of many machine learning methods. We propose ahazard-regularized variational autoencoder that supports straightforwardinterpretation of deep neural architectures in the context of survivalanalysis, a field highly relevant in healthcare. We apply the proposed approachto abdominal CT scans of patients with liver tumors and their correspondingsurvival times.

Survival-oriented embeddings for improving accessibility to complex data structures