Investigation of deep learning neural network architectures for biomedical pattern classification problems

研究生物医学模式分类问题的深度学习神经网络架构

• 批准号: 485676-2015
• 负责人: Henry, Christopher
• 金额: $ 1.82万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=585129
• 关键词: Investigation; deep; learning; neural; network

Recently, there have been many amazing pattern classification breakthroughs achieved by deep learning neural networks. These advancements have been made possible due to Graphics Processing Units (GPU) hardware, which significantly accelerates the deep learning algorithms, and by the availability of data in labelled "big data'" datasets. Consequently, more training can occur due to the increased computational power afforded by GPUs, and these large-scale datasets allow for improved partitioning of high dimensional feature space for classification tasks. The result is that trained neural networks can achieve very low pattern classification error rates. Thus, the aim of this Engage Grant is to investigate deep learning neural network architectures for use by Sightline Innovation Inc. to improve and accelerate the performance of its Sightline Perception Engine, which forms the foundation of Sightline's VuPoint biomedical classification products. This research is important as it represents a significant step toward bridging the gap between the growing mountain of personal health information and automated solutions for tailoring an individual's treatment plan. This work is of importance to Sightline, as well as the biomedical industry, as there are currently very few within the biomedical commercial space investigating the integration of machine learning into biomedicine, in vitro diagnostics, and biosecurity. The anticipated outcomes are a deep learning pattern classification system that fits within Sightline's overall product development map, which will facilitate their diagnosis and detection goals. The benefits to Canada include developing automated systems for personalized medicine, and reducing the money spent annually due to infectious diseases and pandemic risk.

最近，深度学习神经网络实现了许多惊人的模式分类突破， 网络.由于图形处理单元（GPU）硬件，这些进步已经成为可能， 这大大加速了深度学习算法，并通过标记为“大”的数据的可用性， 数据”的数据集。因此，由于由计算能力提供的增加的计算能力，可以进行更多的训练。 GPU和这些大规模数据集允许改进高维特征空间的划分， 分类任务。其结果是，经过训练的神经网络可以实现非常低的模式分类错误 rates.因此，这项Engage Grant的目的是研究深度学习神经网络架构， 视线创新公司提高和加速其视线感知引擎的性能， 构成了Sightline的VuPoint生物医学分类产品的基础。这项研究很重要，因为它 代表着在日益增长的个人健康与健康之间的差距上迈出的重要一步 信息和自动化解决方案，以定制个人的治疗计划。这项工作对于 视线，以及生物医药行业，因为目前有很少的生物医药商业内 空间研究机器学习与生物医学、体外诊断和生物安全的整合。 预期的结果是一个深度学习模式分类系统，适合Sightline的整体 产品开发图，这将促进他们的诊断和检测目标。对加拿大的好处 包括开发个性化医疗的自动化系统，以及减少每年的资金支出， 传染病和流行病的风险。