Research Repository

Deep Learning in Neuroimaging: Effect of Data Leakage in Cross-validation Using 2D Convolutional Neural Networks

Yagis, Ekin and Workalemahu Atnafu, Selamawet and Garcia Seco De Herrera, Alba and Marzi, Chiara and Giannelli, Marco and Tessa, Carlo and Citi, Luca and Diciotti, Stefano (2021) 'Deep Learning in Neuroimaging: Effect of Data Leakage in Cross-validation Using 2D Convolutional Neural Networks.' Scientific Reports, 11 (1). 22544-. ISSN 2045-2322

s41598-021-01681-w.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview


In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson’s Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets.

Item Type: Article
Uncontrolled Keywords: Brain; Humans; Parkinson Disease; Alzheimer Disease; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Case-Control Studies; Cross-Sectional Studies; Reproducibility of Results; Predictive Value of Tests; Aged; Aged, 80 and over; Middle Aged; Female; Male; Neuroimaging; Deep Learning; Neural Networks, Computer
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Social Sciences
Faculty of Social Sciences > Institute for Social and Economic Research
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Dec 2021 15:12
Last Modified: 12 Feb 2022 00:30

Actions (login required)

View Item View Item