Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Estimation in Medical Image Denoising with Bayesian Deep
  Image Prior   Uncertainty quantification in inverse medical imaging tasks with deep
learning has received little attention. However, deep models trained on large
data sets tend to hallucinate and create artifacts in the reconstructed output
that are not anatomically present. We use a randomly initialized convolutional
network as parameterization of the reconstructed image and perform gradient
descent to match the observation, which is known as deep image prior. In this
case, the reconstruction does not suffer from hallucinations as no prior
training is performed. We extend this to a Bayesian approach with Monte Carlo
dropout to quantify both aleatoric and epistemic uncertainty. The presented
method is evaluated on the task of denoising different medical imaging
modalities. The experimental results show that our approach yields
well-calibrated uncertainty. That is, the predictive uncertainty correlates
with the predictive error. This allows for reliable uncertainty estimates and
can tackle the problem of hallucinations and artifacts in inverse medical
imaging tasks.

13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin