论文标题
D'Artagnan：反事实视频生成
D'ARTAGNAN: Counterfactual Video Generation
论文作者
论文摘要
有因果关系的机器学习框架可以通过回答反事实问题来帮助临床医生确定最佳的治疗方法。我们通过研究左心室射血分数的变化，探索超声心动图的情况，这是这些检查中获得的最重要的临床指标。我们首次结合了深层神经网络，双因果网络和生成对抗方法，以建立一种新颖的因果生成模型，建立D'Artagnan（深人造双胞胎架构生成网络）。在将其应用于心脏超声视频之前，我们在合成数据集上证明了方法的合理性，以回答以下问题：“如果患者的射血分数不同，则超声心动图会是什么样？”。为此，我们生成了新的超声视频，保留了原始患者的视频样式和解剖结构，同时修改了以给定输入为条件的射血分数。我们在反事实视频中获得0.79的SSIM分数为0.79，R2得分为0.51。代码和型号可在以下网址提供：https：//github.com/hreynaud/dartagnan。
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.
