Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identify the cells' nuclei based on the deep learning neural network   Identify the cells' nuclei is the important point for most medical analyses.
To assist doctors finding the accurate cell' nuclei location automatically is
highly demanded in the clinical practice. Recently, fully convolutional neural
network (FCNs) serve as the back-bone in many image segmentation, like liver
and tumer segmentation in medical field, human body block in technical filed.
The cells' nuclei identification task is also kind of image segmentation. To
achieve this, we prefer to use deep learning algorithms. we construct three
general frameworks, one is Mask Region-based Convolutional Neural Network (Mask
RCNN), which has the high performance in many image segmentations, one is
U-net, which has the high generalization performance on small dataset and the
other is DenseUNet, which is mixture network architecture with Dense Net and
U-net. we compare the performance of these three frameworks. And we evaluated
our method on the dataset of data science bowl 2018 challenge. For single model
without any ensemble, they all have good performance.

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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
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The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg