• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition >Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers
【24h】

Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers

机译：使用纵向丰富的表示法成像生物标志物预测认知下降。

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With rapid progress in high-throughput genotyping and neuroimaging, researches of complex brain disorders, such as Alzheimer’s Disease (AD), have gained significant attention in recent years. Many prediction models have been studied to relate neuroimaging measures to cognitive status over the progressions when these disease develops. Missing data is one of the biggest challenge in accurate cognitive score prediction of subjects in longitudinal neuroimaging studies. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation for imaging biomarkers that can simultaneously capture both the information conveyed by baseline neuroimaging records and that by progressive variations of varied counts of available follow-up records over time. While the numbers of the brain scans of the participants vary, the learned biomarker representation for every participant is a fixed-length vector, which enable us to use traditional learning models to study AD developments. Our new objective is formulated to maximize the ratio of the summations of a number of L1-norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. A performance gain has been achieved to predict four different cognitive scores, when we compare the original baseline representations against the learned representations with enrichments. These promising empirical results have demonstrated improved performances of our new method that validate its effectiveness.
机译：随着高通量基因分型和神经影像学的快速发展，近年来复杂的脑部疾病（例如阿尔茨海默氏病（AD））的研究受到了广泛关注。已经研究了许多预测模型，这些模型将神经影像学测量与疾病发展过程中的认知状态联系起来。在纵向神经影像研究中，数据的丢失是准确预测受试者的认知得分的最大挑战之一。为了解决这个问题，在本文中，我们提出了一种新颖的方法来学习生物标志物成像的丰富表示，它可以同时捕获基线神经影像记录所传达的信息以及随着时间推移可利用的后续记录的计数不断变化所传递的信息。尽管参与者的大脑扫描次数有所不同，但是每个参与者的学习的生物标志物表示形式都是固定长度的向量，这使我们能够使用传统的学习模型来研究AD的发展。我们制定了新的目标，以最大化多个L1范数距离之和的比率，以提高鲁棒性，但是通常很难有效地解决该问题。因此，我们推导了一种新的高效迭代求解算法，并严格证明了其收敛性。我们已经在阿尔茨海默氏病神经影像学倡议（ADNI）数据集中进行了广泛的实验。当我们将原始的基线表示与学习的表示与丰富的表示进行比较时，可以实现预测四个不同认知得分的性能提升。这些有希望的经验结果证明了我们新方法的改进性能，证实了其有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号