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Smart electronic gastroscope system using a cloud-edge collaborative framework

机译：使用云边缘协作框架的智能电子胃镜系统

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Computer-aided gastroscopic image analysis is capable of reducing the work intensity of gastroscopists, and it is of great significance for improving the sensitivity and specificity of upper gastrointestinal disease screening. However, most computer-aided gastroscope systems provide intelligent image analysis services that rely on public cloud platforms and suffer from high communication and computing costs. Moreover, these systems are normally unavailable for offline clinical practice. In this study, we propose a smart electronic gastroscope system based on a cloud-edge collaborative framework. In this system, edge computing platforms and cloud platforms work collaboratively to achieve real-time lesion localization and fine-grained disease classification of gastroscopic videos. In addition, we propose a novel approach called cloud-edge collaborative dynamic lesion detection for upper gastrointestinal disease inference. First, to assist real-time lesion detection in the offline mode or discover a suspicious frame in the online mode, we develop a Tinier-YOLO algorithm based on the k-DSC module in edge computing platforms. Second, to further improve the modeling performance, we integrate lesion ROI segmentation strategy into the YOLOv3 algorithm in the cloud platform. By testing clinical data, we prove that our approach exhibits superior performance in mAP and IOU of lesion detection and response time of service. (C) 2019 Elsevier B.V. All rights reserved.
机译：计算机辅助胃镜图像分析能够减轻胃镜医师的工作强度，对提高上消化道疾病筛查的敏感性和特异性具有重要意义。但是，大多数计算机辅助胃镜系统都提供依赖于公共云平台的智能图像分析服务，并且通信和计算成本较高。此外，这些系统通常对于离线临床实践不可用。在这项研究中，我们提出了一种基于云边缘协作框架的智能电子胃镜系统。在该系统中，边缘计算平台和云平台协同工作以实现实时病变定位和胃镜视频的细粒度疾病分类。此外，我们提出了一种新的方法，称为云边缘协作动态病变检测，用于上消化道疾病的推断。首先，为了帮助离线模式下的实时病变检测或在线模式下发现可疑帧，我们在边缘计算平台中基于k-DSC模块开发了Tinier-YOLO算法。其次，为了进一步提高建模性能，我们将病灶ROI分割策略集成到了云平台中的YOLOv3算法中。通过测试临床数据，我们证明了我们的方法在病变检测和服务响应时间的mAP和IOU中表现出卓越的性能。 （C）2019 Elsevier B.V.保留所有权利。

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