Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalizing Deep Whole Brain Segmentation for Pediatric and
  Post-Contrast MRI with Augmented Transfer Learning   Generalizability is an important problem in deep neural networks, especially
in the context of the variability of data acquisition in clinical magnetic
resonance imaging (MRI). Recently, the Spatially Localized Atlas Network Tiles
(SLANT) approach has been shown to effectively segment whole brain non-contrast
T1w MRI with 132 volumetric labels. Enhancing generalizability of SLANT would
enable broader application of volumetric assessment in multi-site studies.
Transfer learning (TL) is commonly used to update the neural network weights
for local factors; yet, it is commonly recognized to risk degradation of
performance on the original validation/test cohorts. Here, we explore TL by
data augmentation to address these concerns in the context of adapting SLANT to
anatomical variation and scanning protocol. We consider two datasets: First, we
optimize for age with 30 T1w MRI of young children with manually corrected
volumetric labels, and accuracy of automated segmentation defined relative to
the manually provided truth. Second, we optimize for acquisition with 36 paired
datasets of pre- and post-contrast clinically acquired T1w MRI, and accuracy of
the post-contrast segmentations assessed relative to the pre-contrast automated
assessment. For both studies, we augment the original TL step of SLANT with
either only the new data or with both original and new data. Over baseline
SLANT, both approaches yielded significantly improved performance (signed rank
tests; pediatric: 0.89 vs. 0.82 DSC, p<0.001; contrast: 0.80 vs 0.76, p<0.001).
The performance on the original test set decreased with the new-data only
transfer learning approach, so data augmentation was superior to strict
transfer learning.

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The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: stat