Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty Quantification in Medical Image Segmentation with
  Multi-decoder U-Net   Accurate medical image segmentation is crucial for diagnosis and analysis.
However, the models without calibrated uncertainty estimates might lead to
errors in downstream analysis and exhibit low levels of robustness. Estimating
the uncertainty in the measurement is vital to making definite, informed
conclusions. Especially, it is difficult to make accurate predictions on
ambiguous areas and focus boundaries for both models and radiologists, even
harder to reach a consensus with multiple annotations. In this work, the
uncertainty under these areas is studied, which introduces significant
information with anatomical structure and is as important as segmentation
performance. We exploit the medical image segmentation uncertainty
quantification by measuring segmentation performance with multiple annotations
in a supervised learning manner and propose a U-Net based architecture with
multiple decoders, where the image representation is encoded with the same
encoder, and segmentation referring to each annotation is estimated with
multiple decoders. Nevertheless, a cross-loss function is proposed for bridging
the gap between different branches. The proposed architecture is trained in an
end-to-end manner and able to improve predictive uncertainty estimates. The
model achieves comparable performance with fewer parameters to the integrated
training model that ranked the runner-up in the MICCAI-QUBIQ 2020 challenge.

16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph