Deep Learning für eine Neuroimaging-basierte Krankheitsdiagnostik

• Antragstellerin: Professorin Dr. Kerstin Ritter
• Fachliche Zuordnung: Klinische Neurologie; Neurochirurgie und Neuroradiologie
• Förderung: Förderung von 2017 bis 2020
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 389563835

• Erstellungsjahr: 2021

Zusammenfassung der Projektergebnisse

Keine Zusammenfassung vorhanden

Projektbezogene Publikationen (Auswahl)

• (2018). Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease. Stoyanov D. et al. (eds) Understanding and Interpreting Machine Learning in Medical Image Computing Applications. MLCN 2018, DLF 2018, IMIMIC 2018. Lecture Notes in Computer Science, vol 11038. Springer, Cham
  Rieke, J, Eitel, F, Weygandt, M, Haynes, JD and Ritter, K
  
• (2019). Artificial Intelligence and How to Open the Black Box, in XPOMET©: 360° Next Generation Healthcare edited by Ulrich H. Pieper, Alois G. Steidel, Jochen A. Werner
  Ritter K
  
• (2019). Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer’s Disease Classification. Frontiers in Aging Neuroscience, 11, 194
  Böhle, M, Eitel, F, Weygandt, M., & Ritter, K
  
• (2019). Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer’s disease classification. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. Lecture Notes in Computer Science. Springer, Cham, 2019. 3-11
  Eitel, F, Ritter, K
  
• (2019). Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation. NeuroImage: Clinical 24, 102003
  Eitel, F, Soehler, E, Bellmann-Strobl, J, Brandt, AU, Ruprecht, K, Giess, RM, . . . Ritter, K
  
• (2020). Data and artificial intelligence assessment methods (DAISAM) reference. In Proceedings of the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) - Meeting I 2020
  Oala L, …, Ritter K, et al.
  
• (2021). Evaluating saliency methods on artificial datawith different background types. at MedNeurips workshop
  Budding, C, Eitel, F, Ritter, K, Haufe, S
  
• (2021). Promises and pitfalls of deep neural networks in neuroimaging-based psychiatry. Experimental Neurology, 113608
  Eitel F, Schulz M-A, Seiler M, Walter H, Ritter K
  
• MRI image registration considerably improves CNN- based disease classification. In: Abdulkadir A. et al. (eds) Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science, vol 13001. Springer, Cham
  Klingenberg M, Stark D, Eitel F, Ritter K