Counterfactual Image Synthesis for Discovery of Personalized Predictive
Image Markers
- URL: http://arxiv.org/abs/2208.02311v1
- Date: Wed, 3 Aug 2022 18:58:45 GMT
- Title: Counterfactual Image Synthesis for Discovery of Personalized Predictive
Image Markers
- Authors: Amar Kumar, Anjun Hu, Brennan Nichyporuk, Jean-Pierre R. Falet,
Douglas L. Arnold, Sotirios Tsaftaris, and Tal Arbel
- Abstract summary: We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution.
Our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level.
- Score: 0.293168019422713
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The discovery of patient-specific imaging markers that are predictive of
future disease outcomes can help us better understand individual-level
heterogeneity of disease evolution. In fact, deep learning models that can
provide data-driven personalized markers are much more likely to be adopted in
medical practice. In this work, we demonstrate that data-driven biomarker
discovery can be achieved through a counterfactual synthesis process. We show
how a deep conditional generative model can be used to perturb local imaging
features in baseline images that are pertinent to subject-specific future
disease evolution and result in a counterfactual image that is expected to have
a different future outcome. Candidate biomarkers, therefore, result from
examining the set of features that are perturbed in this process. Through
several experiments on a large-scale, multi-scanner, multi-center multiple
sclerosis (MS) clinical trial magnetic resonance imaging (MRI) dataset of
relapsing-remitting (RRMS) patients, we demonstrate that our model produces
counterfactuals with changes in imaging features that reflect established
clinical markers predictive of future MRI lesional activity at the population
level. Additional qualitative results illustrate that our model has the
potential to discover novel and subject-specific predictive markers of future
activity.
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