Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in
  Medical Imaging   Application of deep neural networks to medical imaging tasks has in some
sense become commonplace. Still, a "thorn in the side" of the deep learning
movement is the argument that deep networks are prone to overfitting and are
thus unable to generalize well when datasets are small (as is common in medical
imaging tasks). One way to bolster confidence is to provide mathematical
guarantees, or bounds, on network performance after training which explicitly
quantify the possibility of overfitting. In this work, we explore recent
advances using the PAC-Bayesian framework to provide bounds on generalization
error for large (stochastic) networks. While previous efforts focus on
classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we
apply these techniques to both classification and segmentation in a smaller
medical imagining dataset: the ISIC 2018 challenge set. We observe the
resultant bounds are competitive compared to a simpler baseline, while also
being more explainable and alleviating the need for holdout sets.

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The categories of this paper are: plasm-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: econ
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org