Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning.

ABSTRACT: Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications in the setting of observational data, deep learning methods must be made compatible with the required causal assumptions. We present a scenario with real-world medical images (CT-scans of lung cancer) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity), respectively. When a deep network would use all the information available in the image to predict surv

SUBMITTER: van Amsterdam WAC 

PROVIDER: S-EPMC6904461 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature