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Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis.值得信赖的临床人工智能解决方案:医学图像分析中深度学习模型不确定性量化的综合综述。
Artif Intell Med. 2024 Apr;150:102830. doi: 10.1016/j.artmed.2024.102830. Epub 2024 Mar 4.
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Role of artificial intelligence in MS clinical practice.人工智能在多发性硬化症临床实践中的作用。
Neuroimage Clin. 2022;35:103065. doi: 10.1016/j.nicl.2022.103065. Epub 2022 May 28.
3
Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset.多发性硬化病变分割来自多位专家:MICCAI 2016 挑战赛数据集。
Neuroimage. 2021 Dec 1;244:118589. doi: 10.1016/j.neuroimage.2021.118589. Epub 2021 Sep 24.
4
Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies.小动物Shanoir(SAS):一种用于管理临床前磁共振脑成像研究的基于云的解决方案。
Front Neuroinform. 2020 May 19;14:20. doi: 10.3389/fninf.2020.00020. eCollection 2020.
5
New OFSEP recommendations for MRI assessment of multiple sclerosis patients: Special consideration for gadolinium deposition and frequent acquisitions.新的 OFSEP 推荐用于多发性硬化症患者的 MRI 评估:特别考虑钆沉积和频繁采集。
J Neuroradiol. 2020 Jun;47(4):250-258. doi: 10.1016/j.neurad.2020.01.083. Epub 2020 Jan 31.
6
Observatoire Français de la Sclérose en Plaques (OFSEP): A unique multimodal nationwide MS registry in France.法国多发性硬化症观察站(OFSEP):法国独一无二的全国性多发性硬化症多模式注册中心。
Mult Scler. 2020 Jan;26(1):118-122. doi: 10.1177/1352458518815602. Epub 2018 Dec 13.
7
Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging.磁共振成像上自动多发性硬化病变分割技术调查。
Comput Med Imaging Graph. 2018 Dec;70:83-100. doi: 10.1016/j.compmedimag.2018.10.002. Epub 2018 Oct 5.
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Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria.多发性硬化症的诊断:2017 年麦当劳标准修订版。
Lancet Neurol. 2018 Feb;17(2):162-173. doi: 10.1016/S1474-4422(17)30470-2. Epub 2017 Dec 21.
9
Longitudinal multiple sclerosis lesion segmentation data resource.纵向多发性硬化病变分割数据资源。
Data Brief. 2017 Apr 8;12:346-350. doi: 10.1016/j.dib.2017.04.004. eCollection 2017 Jun.
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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.采用级联3D卷积神经网络方法改进自动多发性硬化病变分割
Neuroimage. 2017 Jul 15;155:159-168. doi: 10.1016/j.neuroimage.2017.04.034. Epub 2017 Apr 19.

Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation.

作者信息

Commowick Olivier, Combès Benoît, Cervenansky Frédéric, Dojat Michel

机构信息

Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France.

Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France.

出版信息

Front Neurosci. 2023 Mar 14;17:1176625. doi: 10.3389/fnins.2023.1176625. eCollection 2023.

DOI:10.3389/fnins.2023.1176625
PMID:36998735
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10043498/
Abstract
摘要