Hybrid Deep Learning Architectures for the Analysis of Multi-Source Spatiotemporal Biomedical Data

用于分析多源时空生物医学数据的混合深度学习架构

• 批准号: RGPIN-2020-06457
• 负责人: Ashraf, Ahmed
• 金额: $ 2.04万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752025
• 关键词: Hybrid; Deep; Learning; Architectures; Analysis

Biomedical data is extremely hybrid in nature, consisting of multi-modal imaging, genomic, and non-imaging data. Parts of this data can be spatial such as a magnetic resonance imaging (MRI) scan. Other parts can be spatio-temporal such as time series of 3D volumes in MRI perfusion imaging. Genomic data can come in as a raw sequence or a summarized outcome of a genetic assay. To fully harness the potential of this data, a holistic, hybrid, and integrative approach is required. I have two long term research goals: First among them is to develop effective machine learning methodologies to enable better understanding of disparate biomedical data ultimately leading to high impact applications. My second goal is  to develop machine learning techniques to improve image acquisition devices such that they produce data which is more amenable to subsequent analysis. To fulfill this long term vision, I will pursue the following short term objectives in my Discovery program: Objective 1: Design of Hybrid Deep Learning Architectures for Perfusion Imaging and Genomic Data 1.1 Develop multi-branch recurrent and convolutional architectures for imaging and genetic data. 1.2 Develop Bayesian strategies to select architecture hyperparameters in hybrid neural nets. 1.3 Develop reinforcement learning methods to optimize neural architecture for hybrid networks. Objective 2:Design of Deep Learning Architectures for Microwave Tomographic Image Reconstruction 2.1 Develop deep neural networks to be used in conjunction with iterative 3D reconstruction methods. 2.2 Develop regularization strategies for neural networks to reconstruct directly from microwave data. 2.3 Incorporate Markov random fields (MRFs) within deep learning architectures for direct tissue segmentation from microwave data. I anticipate a high impact of my proposed research: (1) The hybrid neural architectures developed under objective 1 will be useful in a variety of biomedical applications where decisions have to be made based on multi-source, multi-format, and multi-modality data. In particular, the developed neural networks will be used for building a technology-based diagnostic tool for early detection of pseudo-progression in brain tumor patients. Pseudoprogression is a phenomenon wherein, the tumor grows in response to the treatment but then subsides. This detection is critical for treatment planning and is difficult for human experts. (2) Microwave imaging uses non-ionizing radiation unlike mammography which uses ionizing radiation, and is currently the standard screening method for breast cancer. The reconstruction algorithms designed under objective 2 would play a central role in the development of a safer breast cancer screening system.  The proposed program will also create unique opportunities to train 3 Ph.D., 2 M.Sc. and 5 undergraduate students in both theoretical and practical aspects of classical machine learning, deep learning, biomedical image analysis, and advanced computing.

生物医学数据本质上是高度混合的，由多模式成像、基因组和非成像数据组成。这些数据的一部分可以是空间的，例如磁共振成像(MRI)扫描。其他部分可以是时空的，例如MRI灌注成像中3D体积的时间序列。基因组数据可以是一个原始序列，也可以是一个基因测试的总结结果。为了充分利用这些数据的潜力，需要一种整体、混合和集成的方法。我有两个长期研究目标：第一个目标是开发有效的机器学习方法，以便更好地理解不同的生物医学数据，最终导致高影响的应用。我的第二个目标是开发机器学习技术，以改进图像采集设备，使它们产生更易于后续分析的数据。为了实现这一长期愿景，我将在我的探索计划中实现以下短期目标：目标1：设计用于灌注成像和基因组数据的混合深度学习架构1.1开发用于成像和遗传数据的多分支递归和卷积架构。1.2发展贝叶斯策略来选择混合神经网络中的结构超参数。1.3开发强化学习方法以优化混合网络的神经结构。目标2：设计用于微波断层图像重建的深度学习结构2.1，开发与迭代三维重建方法相结合的深度神经网络。2.2开发用于神经网络的正则化策略，以便直接从微波数据重建。2.3将马尔可夫随机场(MRF)结合到深度学习结构中，用于直接从微波数据中分割组织。我预计我提出的研究将产生很高的影响：(1)根据目标1开发的混合神经体系结构将在各种生物医学应用中有用，这些应用必须基于多源、多格式和多模式数据做出决策。特别是，开发的神经网络将用于建立基于技术的诊断工具，用于早期检测脑肿瘤患者的假性进展。假性进展是一种现象，在这种现象中，肿瘤在治疗后生长，但随后消退。这种检测对治疗计划至关重要，对人类专家来说也很困难。(2)与使用电离辐射的乳房X光检查不同，微波成像使用非电离辐射，是目前乳腺癌的标准筛查方法。根据目标2设计的重建算法将在开发更安全的乳腺癌筛查系统方面发挥核心作用。拟议的计划还将创造独特的机会，培养3名博士、2名硕士。以及经典机器学习、深度学习、生物医学图像分析和高级计算的理论和实践方面的5名本科生。