Zusammenfassung
This study evaluates the diagnostic performance for binary abnormality classification of deep learning models on various types of sequences from a multidisease clinical brain MRI dataset. Additionally, it determines the influence of the sample size and the type of disease. The sequences are DWI, FLAIR, T1- weighted, T1-weighted FLAIR, T2-weighted and T2-weighted FLAIR. On the full-sized multi-disease, the best performance is achieved on the T2-weighted FLAIR sequence using a VGG-16 dataset resulting in an AUC value of 0.89. The work highlights the importance of carefully selecting MRI sequences for deep learning and identifies discrepancies to screening protocols for physicians.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Pullig, M., Bergner, B., Doshi, A., Hennemuth, A., Fayad, Z.A., Lippert, C. (2022). Deep Learning Models for 3D MRI Brain Classification. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_44
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DOI: https://doi.org/10.1007/978-3-658-36932-3_44
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