Distributed Deep Learning using Blockchain Mining Servers for Medical Imaging

使用区块链挖掘服务器进行医疗成像的分布式深度学习

• 批准号: 529457-2018
• 负责人: Fevens, Thomas
• 金额: $ 1.82万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650691
• 关键词: Distributed; Deep; Learning; using; Blockchain

In recent years, a variety of new deep neural networks (DNN) architectures such as GoogleNet and VGGNet**have been proposed to solve AI problems like classification and object identification. The emergence of larger**public datasets, such as ImageNet, has further spurred artificial intelligence research and lead to the design of**deeper and more expressive DNNs so that the complexity of models is sufficient for the task. Although**graphics processing unites (GPU) provides enormous computational power for deep learning purposes, these**deeper and more complex models require weeks or even months to be trained on a single GPU which has led to**research in distributed GPU training.**In line with this need, Service ECVictor Inc, a subsidiary of Nebula AI Inc., has deployed a novel distributed**computing architecture that converts the hash power from blockchain mining machines (miners) into groups of**GPU computing nodes (each miner containing multiple GPUs). Nebula AI aims to offer a highly efficient, low**cost, safe and reliable computing platform to handle a wide variety of Artificial Intelligence (AI) tasks. For the**current Engage project, Nebula AI will expand the solutions available on their platform (primarily based on**numerical or text-based data) to include solutions based on the distributed training of models for deep learning**approaches using 2D and 3D image data, which are common in deep learning applications for healthcare. To**test these solutions, the solutions will be used to develop computer-aided medical diagnostic systems based on**deep learning for databases of Diabetic Retinopathy examinations. The resulting research publications will**demonstrate the viability of the Nebula AI platform to be used for Deep Learning for Medical Imaging**problems.

近年来，人们提出了各种新的深度神经网络（DNN）架构，如GoogleNet和VGGNet**，以解决分类和对象识别等人工智能问题。更大的公共数据集（如ImageNet）的出现，进一步刺激了人工智能的研究，并导致了更深入、更具表现力的dnn的设计，从而使模型的复杂性足以完成任务。尽管图形处理单元（GPU）为深度学习目的提供了巨大的计算能力，但这些更深层、更复杂的模型需要在单个GPU上训练数周甚至数月，这导致了分布式GPU训练的研究。**根据这一需求，星云AI公司的子公司Service ECVictor公司部署了一种新型的分布式计算架构，将区块链台矿机（矿工）的哈希算力转换为**个GPU计算节点组（每个矿工包含多个GPU）。星云AI旨在提供一个高效、低成本、安全可靠的计算平台来处理各种各样的人工智能（AI）任务。对于当前的Engage项目，Nebula AI将扩展其平台上可用的解决方案（主要基于数字或基于文本的数据），以包括基于使用2D和3D图像数据的深度学习方法的分布式模型训练的解决方案，这在医疗保健的深度学习应用程序中很常见。为了测试这些解决方案，这些解决方案将用于开发基于深度学习的计算机辅助医疗诊断系统，用于糖尿病视网膜病变检查数据库。由此产生的研究出版物将证明Nebula AI平台用于医学成像问题的深度学习的可行性。