Adapt innovative deep learning methods from breast cancer to Alzheimers disease

将创新的深度学习方法从乳腺癌应用到阿尔茨海默病 摘要 阿尔茨海默病（AD）是最常见的痴呆症，受影响的美国人数量 预计到2050年将达到1340万。早期发现和治疗AD对于预防非糖尿病至关重要。 可逆的致命脑损伤因此，从神经影像学方式开发非侵入性标记物 (e.g.,脑MRI）对AD的筛查和早期发现具有重要意义。在PI的活跃R 01奖中， （1 R 01 EB 032896 -01），研究重点是开发一种新的研究战略和技术路线， 分析乳腺癌图像以进行诊断、风险预测和分诊的创新。核心战略是 将医疗/临床智能纳入数据驱动的深度学习建模。这项技术创新是 然而不限于乳腺癌，而是也可以适用于其它疾病，例如AD。所以针对本 补充建议，我们建议通过采用新的技术， 乳腺癌中的创新，为发现早期和无创成像而进行认知结果预测 AD的生物标志物。这项Supplement研究的主要任务是使用大脑MRI构建深度学习模型 作为认知结果预测的输入，其被公式化为三个分类问题中的典型分类问题。 认知类别：正常对照组vs.轻度认知障碍vs. AD。我们提出了两个具体目标： 通过样本的特征知识进行深度课程学习，以预测认知结果， 2)从纵向脑MRI中学习知识以提高对AD的预测。我们将主要使用 阿尔茨海默病神经成像倡议（ADNI）数据集。我们已经建立了一个多- 具有互补专业知识的专业团队。拟议的研究将提供一个途径， 在其他领域开发的创新技术，以推进非侵入性成像生物标志物 发展AD。该项目还将为PI的团队提供参与的机会， 有助于AD相关的新研究。

Adapt innovative deep learning methods from breast cancer to Alzheimer’s disease Abstract Alzheimer’s disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. Early detection and treatment of AD is critical to prevent non- reversible and fatal brain damage. Thus, development of non-invasive markers from neuroimaging modalities (e.g., brain MRI) is of great significance for screening and early detection of AD. In the PI’s active R01 award (1R01EB032896-01), the research focuses on developing a new line of research strategy and technical innovation to analyze breast cancer images for diagnosis, risk prediction, and triage. The core strategy is to incorporate medical/clinical intelligence into data-driven deep learning modeling. This technical innovation is however not limited to breast cancer, but can be adapted to other diseases, such as AD, as well. Thus, in this Supplement proposal, we propose to develop an AD focus of our active R01 by adapting the new technical innovation in breast cancer into cognitive outcome prediction for discovering early and no-invasive imaging biomarkers for AD. The main task of this Supplement study is to build deep learning models using brain MRIs as input for cognitive outcome prediction, which is formulated as a typical classification problem among three cognitive classes: Normal Control vs. Mild Cognitive Impairment vs. AD. We proposed two specific aims: 1) Deep curriculum learning informed by samples’ characteristic knowledge for cognitive outcome prediction and 2) Learning knowledge from longitudinal brain MRIs to improve prediction of AD. We will mainly use the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We have assembled a multi- disciplinary team with complementary expertise. The proposed study will provide an avenue to translate some of the innovative techniques developed in other domains to advance non-invasive imaging biomarker development for AD. This project will also provide an opportunity for the PI’s team to get involved and contribute to AD-related new research.