Multimodal learning for clinical decision support : 11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. ML-CDS (Workshop) (11th : 2021 : Online)最新文献
Pub Date : 2021-10-01Epub Date: 2021-10-20DOI: 10.1007/978-3-030-89847-2_1
Kaitlin M Stouffer, Zhenzhen Wang, Eileen Xu, Karl Lee, Paige Lee, Michael I Miller, Daniel J Tward
Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease-with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of "images" and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a "scattering transform" to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer's Disease-the reliance on invasive methods to detect microscopic pathology.
神经成像技术的进步使可用数据的规模和类型变得多种多样。有效整合这些数据有望加深对解剖学和疾病的理解，并对诊断和治疗产生影响。目前的计算工具和数学框架往往针对特定的数据类型或规模，仍不足以同时注册这些多种模式的 "图像 "并对随之而来的数据进行统计分析。在此，我们提出了（1）一种使用 "散射变换 "对齐高分辨率和低分辨率图像的配准算法，以及（2）一种基于变体的建模框架，用于计算多尺度数据的三维空间统计。我们使用我们的方法量化内侧颞叶宏观三维区域的微观 tau 病理学，以解决阿尔茨海默病诊断中的一大难题--依赖侵入性方法检测微观病理学。
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Pub Date : 2021-01-01DOI: 10.1007/978-3-030-89847-2
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Multimodal learning for clinical decision support : 11th International Workshop, ML-CDS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. ML-CDS (Workshop) (11th : 2021 : Online)
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