Effects Of Mri Scanner Manufacturers In Classification Tasks With Deep Learning Models

This study examines the performance of deep learning-based networks for disease classification tasks using multi-center MRI data from three scanner manufacturers (GE, Philips, and Siemens). Results show a substantial decline in classification performance when models trained on one type of scanner manufacturer are tested with data from different manufacturers. Furthermore, applying ComBat-based harmonization techniques to multi-center datasets of 3D structural MR images does not lead to any noticeable performance enhancement for disease classification tasks.