2021/10/21 by Tobias Weber, Weber, Tobias, Michael Ingrisch +8
Computer Science · Medicine · #AI in cancer detection #Artificial intelligence #Artificial neural network #Autoencoder #COVID-19 diagnosis using AI #Computer science #Computer security #Context (archaeology) #Data science #Deep learning #FOS: Computer and information sciences #Field (mathematics) #Interpretability #Life expectancy #Machine Learning (cs.LG) #Machine learning #Medicine #Radiomics and Machine Learning in Medical Imaging #Transparency (behavior) #cs.LG
paper · pdf · doi:10.48550/arxiv.2110.11303
NeurIPS 2021 Workshop, Bridging the Gap: From Machine Learning Research to Clinical Practice
openalex publication_date 2021/10/21 · arxiv created 2021/11/03 · arxiv updated 2021/11/04 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
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.