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Preface to the Series… Acknowledgements… Preface… Table of Contents… Contributing Authors… Part I Machine Learning Fundamentals 1. A Non-Technical Introduction to Machine Learning Olivier Colliot 2. Classic Machine Learning Methods Johann Faouzi and Olivier Colliot 3. Deep Learning: Basics and Convolutional Neural Networks (CNN) Maria Vakalopoulou, Stergios Christodoulidis, Ninon Burgos, Olivier Colliot, and Vincent Lepetit 4. Recurrent Neural Networks (RNN) – Architectures, Training Tricks, and Introduction to Influential Research Susmita Das, Amara Tariq, Thiago Santos, Sai Sandeep Kantareddy, and Imon Banerjee 5. Generative Adversarial Networks and Other Generative Models Markus Wenzel 6. Transformers and Visual Transformers Robin Courant, Maika Edberg, Nicolas Dufour, and Vicky Kalogeiton Part II Data 7. Clinical Assessment of Brain Disorders Stphane Epelbaum and Federica Cacciamani 8. Neuroimaging in Machine Learning for Brain Disorders Ninon Burgos 9. Electroencephalography and Magnetoencephalography Marie-Constance Corsi 10. Working with Omics Data, An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science Thibault Poinsignon, Pierre Poulain, Mlina Gallopin, and Galle Lelandais 11. Electronic Health Records as Source of Research Data Wenjuan Wang, Davide Ferrari, Gabriel Haddon-Hill, and Vasa Curcin 12. Mobile Devices, Connected Objects, and Sensors Sirenia Lizbeth Mondragn-Gonzlez, Eric Burguire, and Karim N’Diaye Part III Methodologies 13. Medical Image Segmentation using Deep Learning Han Liu, Dewei Hu, Hao Li, and Ipek Oguz 14. Image Registration: Fundamentals and Recent Advances Based on Deep Learning Min Chen, Nicholas J. Tustison, Rohit Jena, and James C. Gee 15. Computer-Aided Diagnosis and Prediction in Brain Disorders Vikram Venkatraghavan, Sebastian R. van der Voort, Daniel Bos, Marion Smits, Frederik Barkhof, Wiro J. Niessen, Stefan Klein, and Esther E. Bron 16. Subtyping Brain Diseases from Imaging Data Junhao Wen, Erdem Varol, Zhijian Yang, Gyujoon Hwang, Dominique Dwyer, Anahita Fathi Kazerooni, Paris Alexandros Lalousis, and Christos Davatzikos 17. Data-Driven Disease Progression Modelling Neil P. Oxtoby 18. Computational Pathology for Brain Disorders Gabriel Jimnez and Daniel Racoceanu 19. Integration of Multimodal Data Marco Lorenzi, Marie Deprez, Irene Balelli, Ana L. Aguila, and Andre Altmann Part IV Validation and Datasets 20. Evaluating Machine Learning Models and their Diagnostic Value Gal Varoquaux and Olivier Colliot 21. Reproducibility in Machine Learning for Medical Imaging Olivier Colliot, Elina Thibeau-Sutre, and Ninon Burgos 22. Interpretability of Machine Learning Methods Applied to Neuroimaging Elina Thibeau-Sutre, Sasha Collin, Ninon Burgos, and Olivier Colliot 23. A Regulatory Science Perspective on Performance Assessment of Machine Learning Algorithms in Imaging Weijie Chen, Daniel Krainak, Berkman Sahiner, and Nicholas Petrick 24. Main Existing Datasets for Open Brain Research on Humans Baptiste Couvy-Duchesne, Simona Bottani, Etienne Camenen, Fang Fang, Mulusew Fikere, Juliana Gonzalez-Astudillo, Joshua Harvey, Ravi Hassanaly, Irfahan Kassam, Penelope A. Lind, Qianwei Liu, Yi Lu, Marta Nabais, Thibault Rolland, Julia Sidorenko, Lachlan Strike, and Margie Wright Part V Disorders 25. Machine Learning for Alzheimer’s Disease and Related Dementias Marc Modat, David M. Cash, Liane Dos Santos Canas, Martina Bocchetta, and Sbastien Ourselin 26. Machine Learning for Parkinson’s Disease and Related Disorders Johann Faouzi, Olivier Colliot, and Jean-Christophe Corvol 27. Machine Learning in Neuroimaging of Epilepsy Hyo Min Lee, Ravnoor Singh Gill, Neda Bernasconi, and Andrea Bernasconi 28. Machine Learning in Multiple Sclerosis Bas Jasperse and Frederik Barkhof 29. Machine Learning for Cerebrovascular Disorders Yannan Yu and David Yen-Ting Chen 30. The Role of Artificial Intelligence in Neuro-Oncology Imaging Jennifer Soun, Lu-Aung Yosuke Masudathaya, Arabdha Biswas, and Daniel S. Chow 31. Machine Learning for Neurodevelopmental Disorders Clara Moreau, Christine Deruelle, and Guillaume Auzias 32. Machine Learning and Brain Imaging for Psychiatric Disorders: New Perspectives Ivan Brossollet, Quentin Gallet, Pauline Favre, and Josselin Houenou Disclosure of Interests of the Editor… Abbreviations… Index…
