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@ARTICLE{Mahmutoglu:301585,
      author       = {M. A. Mahmutoglu and A. Rastogi and G. Brugnara$^*$ and P.
                      Vollmuth$^*$ and M. Foltyn-Dumitru and F. Sahm$^*$ and S.
                      Pfister$^*$ and D. Sturm$^*$ and M. Bendszus and M. Schell},
      title        = {{O}ptimizing {MRI} sequence classification performance:
                      insights from domain shift analysis.},
      journal      = {European radiology},
      volume       = {nn},
      issn         = {0938-7994},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2025-01093},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {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 $(95\%$ CI 0.880-0.904). Expert domain knowledge
                      adjustments further improved the MedViT model's accuracy to
                      0.905 $(95\%$ CI 0.893-0.916), showcasing its robustness in
                      handling domain shift.Advanced neural network architectures
                      like MedViT and expert domain knowledge on the target
                      dataset significantly enhance the performance of MRI
                      sequence classification models under domain shift
                      conditions. By combining the strengths of CNNs and
                      transformers, hybrid architectures offer enhanced robustness
                      for reliable automated MRI sequence classification in
                      diverse research and clinical settings.Question Domain shift
                      between adult and pediatric MRI data limits deep learning
                      model accuracy, requiring solutions for reliable sequence
                      classification across diverse patient populations. Findings
                      The MedViT model outperformed ResNet-18 in pediatric
                      imaging; expert domain knowledge adjustment further improved
                      accuracy, demonstrating robustness across diverse datasets.
                      Clinical relevance This study enhances MRI sequence
                      classification by leveraging advanced neural networks and
                      expert domain knowledge to mitigate domain shift, boosting
                      diagnostic precision and efficiency across diverse patient
                      populations in multicenter environments.},
      keywords     = {Convolutional neural networks (Other) / Deep learning
                      (Other) / Magnetic resonance imaging (Other)},
      cin          = {E230 / B062 / HD01 / B300 / B360},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)B062-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)B300-20160331 /
                      I:(DE-He78)B360-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40418319},
      doi          = {10.1007/s00330-025-11671-5},
      url          = {https://inrepo02.dkfz.de/record/301585},
}