Survival-Oriented Embeddings for Improving Accessibility to Complex Data Structures

MCML Authors

Tobias Weber

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Michael Ingrisch

Prof. Dr.

Core PI

Clinical Data Science in Radiology

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

David Rügamer

Prof. Dr.

Core PI

Statistics, Data Science and Machine Learning

Abstract

Deep learning excels in the analysis of unstructured data and recent advancements allow to extend these techniques to survival analysis. In the context of clinical radiology, this enables, e.g., to relate unstructured volumetric images to a risk score or a prognosis of life expectancy and support clinical decision making. Medical applications are, however, associated with high criticality and consequently, neither medical personnel nor patients do usually accept black box models as reason or basis for decisions. Apart from averseness to new technologies, this is due to missing interpretability, transparency and accountability of many machine learning methods. We propose a hazard-regularized variational autoencoder that supports straightforward interpretation of deep neural architectures in the context of survival analysis, a field highly relevant in healthcare. We apply the proposed approach to abdominal CT scans of patients with liver tumors and their corresponding survival times.

inproceedings WIF+21

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Bridging the Gap: from ML Research to Clinical Practice @NeurIPS 2021

Workshop on Bridging the Gap: from Machine Learning Research to Clinical Practice at the 35th Conference on Neural Information Processing Systems. Virtual, Dec 06-14, 2021.

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Authors

T. Weber • M. Ingrisch • M. Fabritius • B. Bischl • D. Rügamer

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Links

arXiv

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Research Areas

 A1 | Statistical Foundations & Explainability

 C1 | Medicine