GE 256排Revolution CT具有分辨高、速度快、辐射剂量低等优点,但随着日常使用频次增高,会出现一些故障,现将日常出现的3例控制板故障总结如下,供同行参考。1故障一故障现象:机器初始化失败,机架屏幕上显示红色感叹号,无法使用。故障分...GE 256排Revolution CT具有分辨高、速度快、辐射剂量低等优点,但随着日常使用频次增高,会出现一些故障,现将日常出现的3例控制板故障总结如下,供同行参考。1故障一故障现象:机器初始化失败,机架屏幕上显示红色感叹号,无法使用。故障分析与排除:重启机器故障依旧,查看报错信息并检查,发现系统如散热、准直器、高压等部件运行正常,只有数据链状态为“0”(“1”表示正常启动时的目标状态,“0”表示此时的实际状态异常)。分析数据传输路径:探测器→无线接触滑环上发射模块Tx→接收模块Rx→光纤→重建接口板→光纤→重建计算机igc1、igc2。展开更多
本文分析了传统智能网业务生成平台的先天缺陷,并对基于下一代业务网的几种业务生成平台进行了介绍,在其基础上设计了一种基于Parlay X API的业务生成平台的架构。该业务生成平台通过将Parlay X API封装成SBB(ServiceBuilding Block)组...本文分析了传统智能网业务生成平台的先天缺陷,并对基于下一代业务网的几种业务生成平台进行了介绍,在其基础上设计了一种基于Parlay X API的业务生成平台的架构。该业务生成平台通过将Parlay X API封装成SBB(ServiceBuilding Block)组件,采用图形化开发界面X-SCE实现业务的定制开发、跟踪和调试,通过Parlay X协议实现与底层网络资源的交互。展开更多
The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landm...The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.展开更多
基金Supported by the Scientific Research Funds for Forestry Public Welfare of China(201004026)Ministry of Education “Overseas Experts and Scholars” project
文摘本文分析了传统智能网业务生成平台的先天缺陷,并对基于下一代业务网的几种业务生成平台进行了介绍,在其基础上设计了一种基于Parlay X API的业务生成平台的架构。该业务生成平台通过将Parlay X API封装成SBB(ServiceBuilding Block)组件,采用图形化开发界面X-SCE实现业务的定制开发、跟踪和调试,通过Parlay X协议实现与底层网络资源的交互。
文摘The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.
