Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image
  Semantic Segmentaion   One of the time-consuming routine work for a radiologist is to discern
anatomical structures from tomographic images. For assisting radiologists, this
paper develops an automatic segmentation method for pelvic magnetic resonance
(MR) images. The task has three major challenges 1) A pelvic organ can have
various sizes and shapes depending on the axial image, which requires local
contexts to segment correctly. 2) Different organs often have quite similar
appearance in MR images, which requires global context to segment. 3) The
number of available annotated images are very small to use the latest
segmentation algorithms. To address the challenges, we propose a novel
convolutional neural network called Attention-Pyramid network (APNet) that
effectively exploits both local and global contexts, in addition to a
data-augmentation technique that is particularly effective for MR images. In
order to evaluate our method, we construct fine-grained (50 pelvic organs) MR
image segmentation dataset, and experimentally confirm the superior performance
of our techniques over the state-of-the-art image segmentation methods.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph