Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain
  MR Images   Reliably modeling normality and differentiating abnormal appearances from
normal cases is a very appealing approach for detecting pathologies in medical
images. A plethora of such unsupervised anomaly detection approaches has been
made in the medical domain, based on statistical methods, content-based
retrieval, clustering and recently also deep learning. Previous approaches
towards deep unsupervised anomaly detection model patches of normal anatomy
with variants of Autoencoders or GANs, and detect anomalies either as outliers
in the learned feature space or from large reconstruction errors. In contrast
to these patch-based approaches, we show that deep spatial autoencoding models
can be efficiently used to capture normal anatomical variability of entire 2D
brain MR images. A variety of experiments on real MR data containing MS lesions
corroborates our hypothesis that we can detect and even delineate anomalies in
brain MR images by simply comparing input images to their reconstruction.
Results show that constraints on the latent space and adversarial training can
further improve the segmentation performance over standard deep representation
learning.

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The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
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: q-fin
13
The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph