A Lightweight Causal Model for Interpretable Subject-level Prediction
Chiara Mauri Stefano Cerri Oula Puonti Mark Mühlau Koen Van Leemput
Recent years have seen a growing interest in methods for predicting a
variable of interest, such as a subject's diagnosis, from medical images.
Methods based on discriminative modeling excel at making accurate predictions,
but are challenged in their ability to explain their decisions in anatomically
meaningful terms. In this paper, we propose a simple technique for
single-subject prediction that is inherently interpretable. It augments the
generative models used in classical human brain mapping techniques, in which
cause-effect relations can be encoded, with a multivariate noise model that
captures dominant spatial correlations. Experiments demonstrate that the
resulting model can be efficiently inverted to make accurate subject-level
predictions, while at the same time offering intuitive causal explanations of
its inner workings. The method is easy to use: training is fast for typical
training set sizes, and only a single hyperparameter needs to be set by the
user. Our code is available at
https://github.com/chiara-mauri/Interpretable-subject-level-prediction.