LAUSR

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.