| Publikationstyp: | Konferenz: Proceedings |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Machine learning in clinical neuroimaging |
| Herausgeber/-in: | Abdulkadir, Ahmed Bathula, Deepti R. Dvornek, Nicha C. Govindarajan, Sindhuja T. Habes, Mohamad Kumar, Vinod Leonardsen, Esten Wolfers, Thomas Xiao, Yiming |
| DOI: | 10.1007/978-3-031-44858-4 |
| Umfang: | X, 174 |
| Angaben zur Konferenz: | 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), held in conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023 |
| Erscheinungsdatum: | 8-Okt-2023 |
| Reihe: | Lecture Notes in Computer Science |
| Reihenzählung: | 14312 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| ISBN: | 978-3-031-44857-7 978-3-031-44858-4 |
| Sprache: | Englisch |
| Schlagwörter: | Biomedical image analysis; Bioinformatics; Image processing; Computer-assisted diagnostics; Morphometry |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 610.28: Biomedizin, Biomedizinische Technik |
| Zusammenfassung: | The rise of neuroimaging data, bolstered by the rapid advancements in computational resources and algorithms, is poised to drive significant breakthroughs in clinical neuroscience. Notably, deep learning is gaining relevance in this domain. Yet, there’s an imbalance: while computational methods grow in complexity, the breadth and diversity of standard evaluation datasets lag behind. This mismatch could result in findings that don’t generalize to a wider population or are skewed towards dominant groups. To address this, it’s imperative to foster inter-domain collaborations that move state-of-the art methods quickly into clinical research. Bridging the divide between various specialties can pave the way for methodological innovations to smoothly transition into clinical research and ultimately, real-world applications.Ourworkshop aimed to facilitate this by creating a forum for dialogue among engineers, clinicians, and neuroimaging specialists. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) was held on October 8th, 2023, as a satellite event of the 26th International Conference on Medical Imaging Computing & Computer-Assisted Intervention (MICCAI 2023) in Vancouver to continue the yearly recurring dialog between experts in machine learning and clinical neuroimaging. The call for papers was made on May 2nd, 2023, and submissions were closed on July 4th, 2023. Each of the 27 submitted manuscripts was reviewed by three or more program committee members in a double-blinded review process. The sixteen accepted papers showcase the integration of machine learning techniques with clinical neuroimaging data. Studied clinical conditions include Alzheimer’s disease, autism spectrum disorder, stroke, and aging. There is a strong emphasis on deep learning approaches to analysis of structural and functional MRI, positron emission tomography, and computed tomography. Research also delves into multi-modal data synthesis and analysis. The conference encapsulated the blend of methodological innovation and clinical applicability in neuroimaging. The proceedings mirror the hallmarks in the sections “Machine learning” and “Clinical applications”, although all papers carry clinical relevance and provide methodological novelty. For the sixth time, this workshop was put together by a dedicated community of authors, program committee, steering committee, and workshop participants. We thank all creators and attendees for their valuable contributions that made the MLCN 2023 Workshop a success. |
| Weitere Angaben: | This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29075 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Centre for Artificial Intelligence (CAI) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
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Zur Langanzeige
Machine learning in clinical neuroimaging. (2023). In A. Abdulkadir, D. R. Bathula, N. C. Dvornek, S. T. Govindarajan, M. Habes, V. Kumar, E. Leonardsen, T. Wolfers, & Y. Xiao (Eds.), 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), held in conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023. Springer. https://doi.org/10.1007/978-3-031-44858-4
Abdulkadir, A. et al. (eds) (2023) Machine learning in clinical neuroimaging, 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), held in conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023. Cham: Springer. Available at: https://doi.org/10.1007/978-3-031-44858-4.
A. Abdulkadir et al., Eds., Machine learning in clinical neuroimaging. Cham: Springer, 2023. doi: 10.1007/978-3-031-44858-4.
ABDULKADIR, Ahmed, Deepti R. BATHULA, Nicha C. DVORNEK, Sindhuja T. GOVINDARAJAN, Mohamad HABES, Vinod KUMAR, Esten LEONARDSEN, Thomas WOLFERS und Yiming XIAO (Hrsg.), 2023. Machine learning in clinical neuroimaging, 2023. Cham: Springer. ISBN 978-3-031-44857-7
Abdulkadir, Ahmed, Deepti R. Bathula, Nicha C. Dvornek, Sindhuja T. Govindarajan, Mohamad Habes, Vinod Kumar, Esten Leonardsen, Thomas Wolfers, and Yiming Xiao, eds. 2023. Machine Learning in Clinical Neuroimaging. 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), Held in Conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023. Cham: Springer. https://doi.org/10.1007/978-3-031-44858-4.
Abdulkadir, Ahmed, et al., editors. “Machine Learning in Clinical Neuroimaging.” 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), Held in Conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023, Springer, 2023, https://doi.org/10.1007/978-3-031-44858-4.
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