Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part III
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part III
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医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第三部分
DOI：
10.1007/978-3-031-16437-8_69
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Aviles-Rivero A
中科院分区：
文献类型：
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作者：
Aviles-Rivero A
阿尔茨海默病前驱期的自动早期诊断对患者治疗、提高生活质量具有重要意义。我们把这个问题作为一个多模态分类任务来处理。多模态数据提供了更丰富和互补的信息。然而，现有技术只考虑数据与单/多模态成像数据之间的低阶关系。在这项工作中，我们引入了一种新的半监督超图学习框架，用于阿尔茨海默病的诊断。我们的框架允许多模态成像和非成像数据之间的高阶关系，同时需要一个很小的标记集。首先，我们引入了一种双嵌入策略来构造一个保持数据语义的鲁棒超图。我们通过使用基于对比的机制在图像和图形级别强制摄动不变性来实现这一点。其次，通过半显式流动，提出了一种动态调整的超图扩散模型，以提高预测的不确定性。我们通过实验证明，我们的框架能够优于目前的阿尔茨海默病诊断技术。
The automatic early diagnosis of prodromal stages of Alzheimer’s disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer’s disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer’s disease diagnosis.