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

近年来，深度学习神经网络在模式分类方面取得了许多令人惊叹的突破 网络。这些进步由于图形处理单元(GPU)硬件而成为可能， 这显著地加快了深度学习算法的速度，并且由于标签为“BIG”的数据的可用性 因此，由于所提供的计算能力的增加，可以进行更多的训练 GPU，并且这些大规模数据集允许改进对高维要素空间的分区 分类任务。结果表明，训练好的神经网络可以达到很低的模式分类误差 费率。因此，该Engage Grant的目标是研究深度学习神经网络体系结构，以供 视线创新公司改进和加速其视线感知引擎的性能，该引擎 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 代表着朝着弥合日益增长的个人健康山之间的差距迈出了重要的一步 用于定制个人治疗计划的信息和自动化解决方案。这项工作对 视线，以及生物医药行业，因为目前生物医药商业领域内的很少 研究机器学习与生物医学、体外诊断和生物安全相结合的空间。 预期的结果是一个深度学习模式分类系统，符合Sightline的整体 产品开发图，这将有助于他们的诊断和检测目标。给加拿大带来的好处 包括开发个性化医疗的自动化系统，以及减少每年应支付的费用 传染病和大流行风险。

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.