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Machine learning in neuroimaging: Progress and challenges.

作者信息

Davatzikos Christos

机构信息

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, United States.

出版信息

Neuroimage. 2019 Aug 15;197:652-656. doi: 10.1016/j.neuroimage.2018.10.003. Epub 2018 Oct 6.

DOI:10.1016/j.neuroimage.2018.10.003
PMID:30296563
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6499712/
Abstract
摘要

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Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1.基于磁共振影像的放射组学特征可揭示胶质母细胞瘤的三种不同亚型，具有不同的临床和分子特征，提供了超越 IDH1 的预后价值。
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Harmonization of cortical thickness measurements across scanners and sites.跨扫描仪和站点的皮质厚度测量的调和。
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Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns.模式成分建模：理解大脑活动模式的表示结构的一种灵活方法。
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Harmonization of multi-site diffusion tensor imaging data.多部位弥散张量成像数据的调和。
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Transl Psychiatry. 2016 Apr 5;6(4):e775. doi: 10.1038/tp.2016.39.
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HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework.HYDRA：通过多最大间隔判别分析框架揭示成像和遗传模式的异质性。
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