Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty-aware multi-view co-training for semi-supervised medical
  image segmentation and domain adaptation   Although having achieved great success in medical image segmentation, deep
learning-based approaches usually require large amounts of well-annotated data,
which can be extremely expensive in the field of medical image analysis.
Unlabeled data, on the other hand, is much easier to acquire. Semi-supervised
learning and unsupervised domain adaptation both take the advantage of
unlabeled data, and they are closely related to each other. In this paper, we
propose uncertainty-aware multi-view co-training (UMCT), a unified framework
that addresses these two tasks for volumetric medical image segmentation. Our
framework is capable of efficiently utilizing unlabeled data for better
performance. We firstly rotate and permute the 3D volumes into multiple views
and train a 3D deep network on each view. We then apply co-training by
enforcing multi-view consistency on unlabeled data, where an uncertainty
estimation of each view is utilized to achieve accurate labeling. Experiments
on the NIH pancreas segmentation dataset and a multi-organ segmentation dataset
show state-of-the-art performance of the proposed framework on semi-supervised
medical image segmentation. Under unsupervised domain adaptation settings, we
validate the effectiveness of this work by adapting our multi-organ
segmentation model to two pathological organs from the Medical Segmentation
Decathlon Datasets. Additionally, we show that our UMCT-DA model can even
effectively handle the challenging situation where labeled source data is
inaccessible, demonstrating strong potentials for real-world applications.

31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol