Publications

Journal articles

  • Gryshchuk, V., Singh, D., Teipel, S., Dyrba, M., 2024. Contrastive Self-supervised Learning for Neurodegenerative Disorder Classification. Accepted by Frontiers in Neuroinformatics. medRxiv preprint: https://doi.org/10.1101/2024.07.03.24309882
  • Levin, F., Grothe, M.J., Dyrba, M., Franzmeier, N., Teipel, S.J., 2024. Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer’s disease. Neurobiology of Aging 135, 26–38. https://doi.org/10.1016/j.neurobiolaging.2023.12.003
  • Gaubert, M., Dell’Orco, A., Lange, C., Garnier-Crussard, A., Zimmermann, I., Dyrba, M., Duering, M., Ziegler, G., Peters, O., Preis, L., Priller, J., Spruth, E.J., Schneider, A., Fliessbach, K., Wiltfang, J., Schott, B.H., Maier, F., Glanz, W., Buerger, K., Janowitz, D., Perneczky, R., Rauchmann, B.-S., Teipel, S., Kilimann, I., Laske, C., Munk, M.H., Spottke, A., Roy, N., Dobisch, L., Ewers, M., Dechent, P., Haynes, J.D., Scheffler, K., Düzel, E., Jessen, F., Wirth, M., 2023. Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia. Frontiers in Psychiatry 13, 1010273. https://doi.org/10.3389/fpsyt.2022.1010273
  • Dyrba, M., Hanzig, M., Altenstein, S., Bader, S., Ballarini, T., Brosseron, F., Buerger, K., Cantré, D., Dechent, P., Dobisch, L., Düzel, E., Ewers, M., Fliessbach, K., Glanz, W., Haynes, J.-D., Heneka, M.T., Janowitz, D., Keles, D.B., Kilimann, I., Laske, C., Maier, F., Metzger, C.D., Munk, M.H., Perneczky, R., Peters, O., Preis, L., Priller, J., Rauchmann, B., Roy, N., Scheffler, K., Schneider, A., Schott, B.H., Spottke, A., Spruth, E.J., Weber, M.-A., Ertl-Wagner, B., Wagner, M., Wiltfang, J., Jessen, F., Teipel, S.J., 2021. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease. Alzheimer’s Research & Therapy 13, 191. https://doi.org/10.1186/s13195-021-00924-2
  • Levin, F., Ferreira, D., Lange, C., Dyrba, M., Westman, E., Buchert, R., Teipel, S.J., Grothe, M.J., 2021. Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration. Alzheimer’s Research & Therapy 13, 49. https://doi.org/10.1186/s13195-021-00785-9
  • Ramírez, J., Gorriz, J.M., Ortiz, A., Cole, J.H., Dyrba, M. (Eds.), 2020. Deep Learning in Aging Neuroscience. Frontiers Media SA. https://doi.org/10.3389/978-2-88966-281-4

Preprints and technical reports

  • Sigle, S., Werner, P., Schweizer, S., Caldeira, L., Hosch, R., Dyrba, M., Fegeler, C., 2024. Bridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol. Technical Whitepaper. https://doi.org/10.34657/13458
  • Pallath, A.H., Dyrba, M., 2020. Comparison of Convolutional neural network training parameters for detecting Alzheimers disease and effect on visualization. Master Thesis. University of Rostock, Germany. https://arxiv.org/pdf/2008.07981.pdf
  • Sagar, Md Motiur Rahman Sagar, Dyrba, M., 2020. Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer’s Disease. Master Thesis. University of Rostock, Germany. https://arxiv.org/pdf/2009.05023.pdf

Conference papers

  • Hiller, B.C., Bader, S., Singh, D., Kirste, T., Becker, M., Dyrba, M., 2025. Evaluating the Fidelity of Explanations for Convolutional Neural Networks in Alzheimer’s Disease Detection, in: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (Eds.), Bildverarbeitung für die Medizin 2025. Springer Fachmedien Wiesbaden. (in press)
  • Singh, D., Dyrba, M., 2024. Computational Ontology and Visualization Framework for the Visual Comparison of Brain Atrophy Profiles, in: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (Eds.), Bildverarbeitung für die Medizin 2024. Springer Fachmedien Wiesbaden, pp. 149–154. https://doi.org/10.1007/978-3-658-44037-4_43
  • Singh, D., Dyrba, M., 2023. Comparison of CNN Architectures for Detecting Alzheimer’s Disease using Relevance Maps, in:  Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (Eds.), Bildverarbeitung für die Medizin 2023. Springer Fachmedien Wiesbaden, pp. 238–243. https://doi.org/10.1007/978-3-658-41657-7_51
  • Dyrba, M., Hanzig, M., 2021. Interactive Visualization of 3D CNN Relevance Maps to Aid Model Comprehensibility, in: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (Eds.), Bildverarbeitung für die Medizin 2021. Springer Fachmedien Wiesbaden, pp. 317–322. https://doi.org/10.1007/978-3-658-33198-6_77
  • Dyrba, M., Pallath, A.H., Marzban, E.N., 2020. Comparison of CNN Visualization Methods to Aid Model Interpretability for Detecting Alzheimer’s Disease, in: Tolxdorff, T., Deserno, T.M., Handels, H. (Eds.), Bildverarbeitung für die Medizin 2020. Springer Fachmedien Wiesbaden, pp. 307–312. https://doi.org/10.1007/978-3-658-29267-6_68