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首页> 外文期刊>IEEE transactions on industrial informatics >MDMaaS: Medical-Assisted Diagnosis Model as a Service With Artificial Intelligence and Trust
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MDMaaS: Medical-Assisted Diagnosis Model as a Service With Artificial Intelligence and Trust

机译：MDMAAS：医疗辅助诊断模式作为人工智能和信任的服务

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摘要

Artificial intelligence has achieved great success in the field of medical-assisted diagnosis, and a deep learning technology plays a very important role in medical image recognition. However, it usually takes medical institutions extra time, energy, and cost to obtain a credible and efficient deep learning model, which is not conducive to a wide range of applications, including medical image recognition and medical decision making. In this article, we propose a novel medical-assisted diagnosis model as a service (MDMaaS). Medical institutions can obtain and use the medical-assisted diagnosis models from the service providers directly; a model training and a model application in machine learning are assigned to a service provider and a consumer, respectively. We have designed a model acquisition method based on the conventional samples and small samples for MDMaaS providers, and we have also developed a trustworthy model-based recommendation method for MDMaaS consumers, which would help the medical institutions to obtain the reliable medical-assisted diagnosis models quickly and efficiently. Based on the MDMaaS, extensive experiments are performed to verify the effectiveness of the proposed method.
机译：人工智能在医疗辅助诊断领域取得了巨大成功，深入学习技术在医学形象识别中发挥着非常重要的作用。然而，它通常需要医疗机构额外的时间，能源和成本，以获得可靠和有效的深度学习模式，这不利于各种应用，包括医学图像识别和医学决策。在本文中，我们提出了一种作为服务（MDMAA）的新型医疗辅助诊断模式。医疗机构可​​以直接从服务提供商中获取和使用医疗辅助诊断模型;机器学习中的模型培训和模型应用程序分别分配给服务提供商和消费者。我们设计了一种基于MDMAAS提供商的传统样本和小型样本的模型采集方法，我们还为MDMAAS消费者制定了一种基于标准的模型建议方法，这将有助于医疗机构获得可靠的医疗辅助诊断模型快速有效地。基于MDMAAS，进行广泛的实验以验证所提出的方法的有效性。

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