English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the application of deep learning on optical coherence tomography (OCT)data, it is common to train classification networks using 2D images originatingfrom 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 trainingand testing sets, with a large portion of the literature overlooking thisaspect. In this study, the effect of improper dataset splitting on modelevaluation is demonstrated for three classification tasks using three OCTopen-access datasets extensively used, Kermany's and Srinivasan's ophthalmologydatasets, and AIIMS breast tissue dataset. Results show that the classificationperformance is inflated by 0.07 up to 0.43 in terms of Matthews CorrelationCoefficient (accuracy: 5% to 30%) for models tested on datasets with impropersplitting, highlighting the considerable effect of dataset handling on modelevaluation. This study intends to raise awareness on the importance of datasetsplitting given the increased research interest in implementing deep learningon OCT data.

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images