Keywords: Variational inference, Hallucination, Deep learning
TL;DR: We propose a novel MFVI approach to deep image prior for medical image post-processing and show its effectiveness on different tasks and modalities.
Abstract: Exploiting the deep image prior property of convolutional auto-encoder networks is especially interesting for medical image processing as it avoids hallucinations by omitting supervised learning. Its spectral bias towards lower frequencies makes it suitable for inverse image problems such as denoising and super-resolution, but manual early stopping has to be applied to act as a low-pass filter. In this paper, we present a novel Bayesian approach to deep image prior using mean-field variational inference. This allows for uncertainty quantification on a per-pixel level and, given the right prior distribution on the network weights, omits the need for early stopping. We optimize the parameters of the weight prior towards reconstruction accuracy using Bayesian optimization with Gaussian Process regression. We evaluate our approach on different inverse tasks on a variety of modalities and demonstrate that an optimized weight prior outperforms former state-of-the-art Bayesian deep image prior approaches. We show that a badly selected prior leads to worse accuracy and calibration and that it is sufficient to optimize the weight prior parameter per task domain.
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Source Code Url: https://github.com/maltetoelle/mfvi-dip
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Data Set Url: https://github.com/maltetoelle/mfvi-dip/tree/main/data
Paper Type: methodological development
Source Latex: zip
Primary Subject Area: Uncertainty Estimation
Secondary Subject Area: Image Acquisition and Reconstruction