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Multi-Modal Multi-Task Learning for Joint Prediction of Clinical Scores in Alzheimer's Disease

机译：联合预测阿尔茨海默氏病临床评分的多模式多任务学习

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One recent interest in computer-aided diagnosis of neurological diseases is to predict the clinical scores from brain images. Most existing methods usually estimate multiple clinical variables separately, without considering the useful correlation information among them. On the other hand, nearly all methods use only one modality of data (mostly structural MRI) for regression, and thus ignore the complementary information among different modalities. To address these issues, in this paper, we present a general methodology, namely Multi-Modal Multi-Task (M3T) learning, to jointly predict multiple variables from multi-modal data. Our method contains three major subsequent steps: (1) a multi-task feature selection which selects the common subset of relevant features for the related multiple clinical variables from each modality; (2) a kernel-based multimodal data fusion which fuses the above-selected features from all modalities; (3) a support vector regression which predicts multiple clinical variables based on the previously learnt mixed kernel. Experimental results on ADNI dataset with both imaging modalities (MRI and PET) and biological modality (CSF) validate the efficacy of the proposed M3T learning method.

机译：在计算机辅助的神经系统疾病诊断中，最近的一项兴趣是从大脑图像预测临床评分。大多数现有方法通常会分别估计多个临床变量，而不考虑其中的有用相关信息。另一方面，几乎所有方法都仅使用一种数据形式（主要是结构MRI）进行回归，因此忽略了不同形式之间的补充信息。为了解决这些问题，在本文中，我们提出了一种通用方法，即多模态多任务（M3T）学习，以从多模态数据中共同预测多个变量。我们的方法包括以下三个主要步骤：（1）多任务特征选择，它从每种方式中为相关的多个临床变量选择相关特征的公共子集；（2）基于内核的多模式数据融合，融合了所有模式中的上述特征；（3）支持向量回归，它基于先前学习的混合核预测多个临床变量。在具有成像方式（MRI和PET）和生物方式（CSF）的ADNI数据集上的实验结果验证了所提出的M3T学习方法的有效性。

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《Multimodal Brain Image Analysis》|2011年|p.60-67|共8页
会议地点 Toronto(CA);Toronto(CA);Toronto(CA);Toronto(CA)
作者
Daoqiang Zhang; Dinggang Shen;
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作者单位

Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599,Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599;

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原文格式 PDF
正文语种 eng
中图分类医用物理学;脑部疾病;
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专利

1. Erratum to multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease [Neuroimage 59/2 (2012) 895-907] [J] . ZhangD., ShenD. NeuroImage . 2012,第3期

机译：对多模式多任务学习进行勘误，以联合预测阿尔茨海默氏病的多个回归和分类变量[Neuroimage 59/2（2012）895-907]
2. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. [J] . Zhang D, Shen D NeuroImage . 2012,第2期

机译：多模式多任务学习，用于联合预测阿尔茨海默氏病的多个回归和分类变量。
3. Joint detection and clinical score prediction in Parkinson's disease via multi-modal sparse learning [J] . Lei Haijun, Huang Zhongwei, Zhang Jian, Expert Systems with Application . 2017,第SEPa期

机译：通过多模式稀疏学习进行帕金森氏病的联合检测和临床评分预测
4. Multi-Modal Multi-Task Learning for Joint Prediction of Clinical Scores in Alzheimer's Disease [C] . Daoqiang Zhang, Dinggang Shen International Workshop on Multimodal Brain Image Analysis . 2011

机译：阿尔茨海默病临床分数联合预测的多模态多任务学习
5. Multi-task Learning for Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets [D] . Wang, Weixin 2015

机译：多任务学习对轮对剩余使用寿命和失效类型的联合预测
6. Multi-Modal Multi-Task Learning for Joint Prediction of Multiple Regression and Classification Variables in Alzheimer’s Disease [O] . Daoqiang Zhang, Dinggang Shen -1

机译：多模态多任务学习用于在阿尔茨海默病中多元回归和分类变量的联合预测
7. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease [O] . Daoqiang Zhang, Dinggang Shen 2012

机译：多模态多任务学习，用于在阿尔茨海默病中多元回归和分类变量的联合预测

Multi-Modal Multi-Task Learning for Joint Prediction of Clinical Scores in Alzheimer's Disease

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