Deep learning for neuroimaging-based disease decoding

• Applicant: Professorin Dr. Kerstin Ritter
• Subject Area: Clinical Neurology; Neurosurgery and Neuroradiology
• Term: from 2017 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 389563835

• Final Report Year: 2021

Final Report Abstract

No abstract available

Publications

• (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