Distributed Deep Learning using Blockchain Mining Servers for Medical Imaging

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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.