Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical
  Segmentation   Deep learning models such as convolutional neural net- work have been widely
used in 3D biomedical segmentation and achieve state-of-the-art performance.
However, most of them often adapt a single modality or stack multiple
modalities as different input channels. To better leverage the multi-
modalities, we propose a deep encoder-decoder structure with cross-modality
convolution layers to incorporate different modalities of MRI data. In
addition, we exploit convolutional LSTM to model a sequence of 2D slices, and
jointly learn the multi-modalities and convolutional LSTM in an end-to-end
manner. To avoid converging to the certain labels, we adopt a re-weighting
scheme and two-phase training to handle the label imbalance. Experimental
results on BRATS-2015 show that our method outperforms state-of-the-art
biomedical segmentation approaches.

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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: math-ph
30
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: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex