Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis   Dermoscopic skin images are often obtained with different imaging devices,
under varying acquisition conditions. In this work, instead of attempting to
perform intensity and color normalization, we propose to leverage computational
color constancy techniques to build an artificial data augmentation technique
suitable for this kind of images. Specifically, we apply the \emph{shades of
gray} color constancy technique to color-normalize the entire training set of
images, while retaining the estimated illuminants. We then draw one sample from
the distribution of training set illuminants and apply it on the normalized
image. We employ this technique for training two deep convolutional neural
networks for the tasks of skin lesion segmentation and skin lesion
classification, in the context of the ISIC 2017 challenge and without using any
external dermatologic image set. Our results on the validation set are
promising, and will be supplemented with extended results on the hidden test
set when available.

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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an