Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inflation of test accuracy due to data leakage in deep learning-based
  classification of OCT images   In the application of deep learning on optical coherence tomography (OCT)
data, it is common to train classification networks using 2D images originating
from volumetric data. Given the micrometer resolution of OCT systems,
consecutive images are often very similar in both visible structures and noise.
Thus, an inappropriate data split can result in overlap between the training
and testing sets, with a large portion of the literature overlooking this
aspect. In this study, the effect of improper dataset splitting on model
evaluation is demonstrated for two classification tasks using two OCT
open-access datasets extensively used in the literature, Kermany's
ophthalmology dataset and AIIMS breast tissue dataset. Our results show that
the classification accuracy is inflated by 3.9 to 26 percentage units for
models tested on a dataset with improper splitting, highlighting the
considerable effect of dataset handling on model evaluation. This study intends
to raise awareness on the importance of dataset splitting for research on deep
learning using OCT data and volumetric data in general.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom