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Titlebook: Machine Learning in Clinical Neuroimaging; 6th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao Conference proceedings 2023

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發(fā)表于 2025-3-21 19:12:02 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning in Clinical Neuroimaging
副標題6th International Wo
編輯Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao
視頻videohttp://file.papertrans.cn/621/620661/620661.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning in Clinical Neuroimaging; 6th International Wo Ahmed Abdulkadir,Deepti R. Bathula,Yiming Xiao Conference proceedings 2023
描述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)..The papers are categorzied into topical sub-headings on Machine Learning and Clinical Applications..
出版日期Conference proceedings 2023
關鍵詞artificial intelligence; bioinformatics; computer networks; computer science; computer systems; computer
版次1
doihttps://doi.org/10.1007/978-3-031-44858-4
isbn_softcover978-3-031-44857-7
isbn_ebook978-3-031-44858-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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VesselShot: Few-shot Learning for?Cerebral Blood Vessel Segmentationerages knowledge from a few annotated support images and mitigates the scarcity of labeled data and the need for extensive annotation in cerebral blood vessel segmentation. We evaluated the performance of VesselShot using the publicly available TubeTK dataset for the segmentation task, achieving a mean Dice coefficient (DC) of ..
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Learning Sequential Information in?Task-Based fMRI for?Synthetic Data Augmentationl information. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task. The results show that the synthetic task-based fMRI can provide effective data augmentation in learning the ASD classification task.
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Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencodera dataset consisting of 5,824 CT images from 3,573 patients across two medical centers with minimal labels. Comparative experiments show that our method achieves the best performance for predicting next-day severity and functional outcome at discharge.
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Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Neten morphological and functional networks at the lowest rank (2). Morphology-function network commonality was retained across all ranks in the visual cortex, but broader network organization diverged between morphology and function at higher ranks.
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0302-9743 ld 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 re
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