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  2. School of Engineering
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dc.contributor.editorAbdulkadir, Ahmed-
dc.contributor.editorBathula, Deepti R.-
dc.contributor.editorDvornek, Nicha C.-
dc.contributor.editorGovindarajan, Sindhuja T.-
dc.contributor.editorHabes, Mohamad-
dc.contributor.editorKumar, Vinod-
dc.contributor.editorLeonardsen, Esten-
dc.contributor.editorWolfers, Thomas-
dc.contributor.editorXiao, Yiming-
dc.date.accessioned2023-11-12T15:25:18Z-
dc.date.available2023-11-12T15:25:18Z-
dc.date.issued2023-10-08-
dc.identifier.isbn978-3-031-44857-7de_CH
dc.identifier.isbn978-3-031-44858-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29075-
dc.descriptionThis 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).de_CH
dc.description.abstractThe 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.de_CH
dc.format.extentX, 174de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectBiomedical image analysisde_CH
dc.subjectBioinformaticsde_CH
dc.subjectImage processingde_CH
dc.subjectComputer-assisted diagnosticsde_CH
dc.subjectMorphometryde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc610.28: Biomedizin, Biomedizinische Technikde_CH
dc.titleMachine learning in clinical neuroimagingde_CH
dc.typeKonferenz: Proceedingsde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-031-44858-4de_CH
zhaw.conference.details6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN), held in conjunction with MICCAI 2023, Vancouver, Canada, 8-12 October 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number14312de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.display.portraitYesde_CH
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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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