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Modal Uncertainty Estimation for Medical Imaging Based Diagnosis

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12959)

Abstract

Medical image based diagnosis is constantly faced with uncertainties. In an ambiguous scenario, different experts will reach different conclusions from their initial assumptions. It is thus important for machine learning models to be capable of proposing different plausible predictions, along with meaningful uncertainty measures. In this work we propose such a novel learning-based framework, named modal uncertainty estimation (MUE), to learn such one-to-many relationship with faithful uncertainty estimation in the medical image understanding tasks. Technically, MUE is based on conditional generative models, but it crucially uses a set of discrete latent variables, each representing a latent mode hypothesis that explains one type of input-output relationship. We justify the use of discrete latent variables by the multi-modal posterior collapse problem in the common conditional generative models. Consequently, MUE can estimate the uncertainty effectively. MUE demonstrates significantly more accurate uncertainty estimation for one-to-many relationship than the current state-of-the-art, and is more informative for practical use. We validate these points on both real and synthetic tasks.

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    https://github.com/sylqiu/modal_uncertainty.

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Qiu, D., Lui, L.M. (2021). Modal Uncertainty Estimation for Medical Imaging Based Diagnosis. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-87735-4_1

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