Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding New Diagnostic Information for Detecting Glaucoma using Neural
  Networks   We describe a new approach to automated Glaucoma detection in 3D Spectral
Domain Optical Coherence Tomography (OCT) optic nerve scans. First, we gathered
a unique and diverse multi-ethnic dataset of OCT scans consisting of glaucoma
and non-glaucomatous cases obtained from four tertiary care eye hospitals
located in four different countries. Using this longitudinal data, we achieved
state-of-the-art results for automatically detecting Glaucoma from a single raw
OCT using a 3D Deep Learning system. These results are close to human doctors
in a variety of settings across heterogeneous datasets and scanning
environments. To verify correctness and interpretability of the automated
categorization, we used saliency maps to find areas of focus for the model.
Matching human doctor behavior, the model predictions indeed correlated with
the conventional diagnostic parameters in the OCT printouts, such as the
retinal nerve fiber layer. We further used our model to find new areas in the
3D data that are presently not being identified as a diagnostic parameter to
detect glaucoma by human doctors. Namely, we found that the Lamina Cribrosa
(LC) region can be a valuable source of helpful diagnostic information
previously unavailable to doctors during routine clinical care because it lacks
a quantitative printout. Our model provides such volumetric quantification of
this region. We found that even when a majority of the RNFL is removed, the LC
region can distinguish glaucoma. This is clinically relevant in high myopes,
when the RNFL is already reduced, and thus the LC region may help differentiate
glaucoma in this confounding situation. We further generalize this approach to
create a new algorithm called DiagFind that provides a recipe for finding new
diagnostic information in medical imagery that may have been previously
unusable by doctors.

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The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin