D'ARTAGNAN: Counterfactual Video Generation
This work addresses a specific problem for clinicians by enabling causal analysis in medical imaging, though it is incremental as it combines existing methods in a novel way for a domain-specific application.
The paper tackles the problem of generating counterfactual echocardiogram videos to answer what-if questions about patient treatments, specifically modifying the Left Ventricle Ejection Fraction while retaining original patient anatomy and style, achieving an SSIM score of 0.79 and an R2 score of 0.51.
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions. We explore this path for the case of echocardiograms by looking into the variation of the Left Ventricle Ejection Fraction, the most essential clinical metric gained from these examinations. We combine deep neural networks, twin causal networks and generative adversarial methods for the first time to build D'ARTAGNAN (Deep ARtificial Twin-Architecture GeNerAtive Networks), a novel causal generative model. We demonstrate the soundness of our approach on a synthetic dataset before applying it to cardiac ultrasound videos to answer the question: "What would this echocardiogram look like if the patient had a different ejection fraction?". To do so, we generate new ultrasound videos, retaining the video style and anatomy of the original patient, while modifying the Ejection Fraction conditioned on a given input. We achieve an SSIM score of 0.79 and an R2 score of 0.51 on the counterfactual videos. Code and models are available at: https://github.com/HReynaud/dartagnan.