Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards increased trustworthiness of deep learning segmentation methods
  on cardiac MRI   Current state-of-the-art deep learning segmentation methods have not yet made
a broad entrance into the clinical setting in spite of high demand for such
automatic methods. One important reason is the lack of reliability caused by
models that fail unnoticed and often locally produce anatomically implausible
results that medical experts would not make. This paper presents an automatic
image segmentation method based on (Bayesian) dilated convolutional networks
(DCNN) that generate segmentation masks and spatial uncertainty maps for the
input image at hand. The method was trained and evaluated using segmentation of
the left ventricle (LV) cavity, right ventricle (RV) endocardium and myocardium
(Myo) at end-diastole (ED) and end-systole (ES) in 100 cardiac 2D MR scans from
the MICCAI 2017 Challenge (ACDC). Combining segmentations and uncertainty maps
and employing a human-in-the-loop setting, we provide evidence that image areas
indicated as highly uncertain regarding the obtained segmentation almost
entirely cover regions of incorrect segmentations. The fused information can be
harnessed to increase segmentation performance. Our results reveal that we can
obtain valuable spatial uncertainty maps with low computational effort using
DCNNs.

9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: q-fin