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Titlebook: Bayesian and grAphical Models for Biomedical Imaging; First International M. Jorge Cardoso,Ivor Simpson,Annemie Ribbens Conference proceed

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發(fā)表于 2025-3-21 19:44:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian and grAphical Models for Biomedical Imaging
期刊簡稱First International
影響因子2023M. Jorge Cardoso,Ivor Simpson,Annemie Ribbens
視頻videohttp://file.papertrans.cn/182/181892/181892.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Bayesian and grAphical Models for Biomedical Imaging; First International  M. Jorge Cardoso,Ivor Simpson,Annemie Ribbens Conference proceed
影響因子This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014..The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
Pindex Conference proceedings 2014
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發(fā)表于 2025-3-21 23:48:44 | 只看該作者
Conference proceedings 2014ical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.
板凳
發(fā)表于 2025-3-22 01:43:53 | 只看該作者
Lecture Notes in Computer Sciencere weighted ?.-norm minimization. Experiments on a digital phantom and . tongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.
地板
發(fā)表于 2025-3-22 06:04:47 | 只看該作者
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發(fā)表于 2025-3-22 09:33:31 | 只看該作者
A Bayesian Approach to Distinguishing Interdigitated Muscles in the Tongue from Limited Diffusion Wre weighted ?.-norm minimization. Experiments on a digital phantom and . tongue diffusion data demonstrate that the proposed method is able to resolve crossing fibers with limited gradient directions.
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發(fā)表于 2025-3-22 15:37:40 | 只看該作者
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發(fā)表于 2025-3-22 20:55:35 | 只看該作者
N3 Bias Field Correction Explained as a Bayesian Modeling Method,l strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable of producing bias field correction results comparable to those of N3 in less computation time.
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發(fā)表于 2025-3-22 22:42:27 | 只看該作者
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發(fā)表于 2025-3-23 02:03:52 | 只看該作者
Four Neuroimaging Questions that P-Values Cannot Answer (and Bayesian Analysis Can),that we formulate as four research questions insoluble with .-values. We demonstrate how, in theory, Bayesian approaches can provide answers to such questions. We discuss the implications of these questions as well as the practicalities of such approaches in neuroimaging.
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發(fā)表于 2025-3-23 09:10:54 | 只看該作者
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