TY  - JOUR
AU  - Mahmutoglu, Mustafa Ahmed
AU  - Rastogi, Aditya
AU  - Brugnara, Gianluca
AU  - Vollmuth, Philipp
AU  - Foltyn-Dumitru, Martha
AU  - Sahm, Felix
AU  - Pfister, Stefan
AU  - Sturm, Dominik
AU  - Bendszus, Martin
AU  - Schell, Marianne
TI  - Optimizing MRI sequence classification performance: insights from domain shift analysis.
JO  - European radiology
VL  - nn
SN  - 0938-7994
CY  - Heidelberg
PB  - Springer
M1  - DKFZ-2025-01093
SP  - nn
PY  - 2025
N1  - epub
AB  - MRI sequence classification becomes challenging in multicenter studies due to variability in imaging protocols, leading to unreliable metadata and requiring labor-intensive manual annotation. While numerous automated MRI sequence identification models are available, they frequently encounter the issue of domain shift, which detrimentally impacts their accuracy. This study addresses domain shift, particularly from adult to pediatric MRI data, by evaluating the effectiveness of pre-trained models under these conditions.This retrospective and multicentric study explored the efficiency of a pre-trained convolutional (ResNet) and CNN-Transformer hybrid model (MedViT) to handle domain shift. The study involved training ResNet-18 and MedVit models on an adult MRI dataset and testing them on a pediatric dataset, with expert domain knowledge adjustments applied to account for differences in sequence types.The MedViT model demonstrated superior performance compared to ResNet-18 and benchmark models, achieving an accuracy of 0.893 (95
KW  - Convolutional neural networks (Other)
KW  - Deep learning (Other)
KW  - Magnetic resonance imaging (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40418319
DO  - DOI:10.1007/s00330-025-11671-5
UR  - https://inrepo02.dkfz.de/record/301585
ER  -