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.