• Mila Glavaški and Lazar Velicki.
• Mila Glavaški, Hajduk Veljkova 3, 21000 Novi Sad, Serbia, milaglavaski@yahoo.com.
• Croat. Med. J. 2022 Aug 31; 63 (4): 370-378.

AimTo assess the effects of different test-set design strategies for magnetic resonance (MR) image classification using deep learning.MethodsError rates in 10 experimental settings were assessed. The performance of pretrained models and data augmentation were examined as possible contributing factors.ResultsError rates in experimental settings using MR images of different patients for training and test sets were ten times higher than those in experimental settings using MR images of the same patients (four disease groups with whole-chest images, 46.80% vs 2.06%; four disease groups without whole-chest images, 49.09% vs 1.29%; sex classification with whole-chest images, 16.02% vs 0.96%; and sex classification without whole-chest images, 23.56% vs 0.30%). Error rates were higher when data augmentation was applied to settings that used MR images of different patients for training and test sets.ConclusionWhen deep learning is applied to MR image classification, training and test sets should consist of MR images of different patients. Models built on training and test sets consisting of images of the same patients yield optimistic error rates and lead to wrong conclusions. MR images of neighboring slices are so similar that they cause data leakage effect.

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