Validating uncertainty in medical image translation

• URL: http://arxiv.org/abs/2002.04639v1
• Date: Tue, 11 Feb 2020 19:06:54 GMT
• Title: Validating uncertainty in medical image translation
• Authors: Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass
• Abstract summary: We investigate using dropout to estimate uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.
• Score: 7.565565370757736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.

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