You are all cordially invited to the AMLab seminar on Thursday September 5th at 16:00 in C3.163, where Wouter van Amsterdam will give a talk titled “Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning”. Afterwards there are the usual drinks and snacks!
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, deep learning methods must be promoted from the level of mere associations to causal questions. We present a scenario with real-world medical images (CT-scans of lung cancers) 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. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long standing goal of personalized medicine supported by artificial intelligence.