2021 11th International Conference on Information Technology in Medicine and Education (ITME)最新文献

Automatic segmentation of glands in infrared meibomian gland image 红外睑板腺图像中腺体的自动分割

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00086

Zhiming Lin, Jiawen Lin, Li Li

引用次数: 0

Node Importance Evaluation Method for Cyberspace Security Risk Control 网络空间安全风险控制的节点重要性评价方法

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00036

Jiaxin Yao, Bihai Lin, Ruiqi Huang, Junyi Fan, Biqiong Chen, Yanhua Liu

{"title":"Node Importance Evaluation Method for Cyberspace Security Risk Control","authors":"Jiaxin Yao, Bihai Lin, Ruiqi Huang, Junyi Fan, Biqiong Chen, Yanhua Liu","doi":"10.1109/ITME53901.2021.00036","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00036","url":null,"abstract":"With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"62 1","pages":"127-131"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75389046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Optimized Detection Method for Siberian crane (Grus leucogeranus) Based on Yolov5 基于Yolov5的西伯利亚鹤检测优化方法

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00031

Wang Linlong, Zhang Huaiqing, Yang Tingdong, Zhang Jing, Cui Zeyu, Zhu Nianfu, Liu Yang, Zuo Yuanqing, Zhang Huacong

{"title":"Optimized Detection Method for Siberian crane (Grus leucogeranus) Based on Yolov5","authors":"Wang Linlong, Zhang Huaiqing, Yang Tingdong, Zhang Jing, Cui Zeyu, Zhu Nianfu, Liu Yang, Zuo Yuanqing, Zhang Huacong","doi":"10.1109/ITME53901.2021.00031","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00031","url":null,"abstract":"In our study, we have explored the influence of panoramic images and ordinary images on the performance of Siberian crane detection, and compared the detection accuracy under different networks based on YOLOv5, to get fine and high-quality datasets and select the proper model for Serbian crane detection. The results show that (i) Training datasets from the internet and ordinary field photos can achieve a better detection performance than other training datasets, and Training datasets from panoramic images only show low accuracy due to Siberian crane's alertness and mosaic data enhancement method adopted in YOLOv5, which reduced the size of a small target. (ii) when the iteration times reach 40000, the YOLOv5 model can completely converge, and the mAP value reached 81.4%, total loss value 0.0357; (iii) With increasing the width and depth of layer in YOLOv5, the value of mAP show a growth trend, however the FPS show an opposite trend; (iv) through verification, we found that the model can also have an effectively performance of detection in the complex environments, such as multi-objective small objects and occlusions, the color similarity between target and background, different dynamic activities including flying, falling, foraging, playing, etc.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"10 1","pages":"01-06"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74287532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Assistant Diagnostic Method of TCM Based on BERT 基于BERT的中医辅助诊断方法研究

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00065

Chuanjie Xu, Feng Yuan, Shouqiang Chen

引用次数: 2

TransDE: A Transformer and Double Encoder Network for Medical Image Segmentation TransDE:用于医学图像分割的变压器和双编码器网络

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00081

Zhaohong Huang, Jiajia Liao, Jun Wei, Guorong Cai, Guowei Zhang

{"title":"TransDE: A Transformer and Double Encoder Network for Medical Image Segmentation","authors":"Zhaohong Huang, Jiajia Liao, Jun Wei, Guorong Cai, Guowei Zhang","doi":"10.1109/ITME53901.2021.00081","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00081","url":null,"abstract":"Over the past decade, medical image segmentation has become a necessary prerequisite for disease diagnosis and treatment planning. The deep convolutional neural networks (CNN) have been widely adopted in medical image segmentation which achieves promising performance. However, due to the intrinsic locality of convolution operations, CNN demonstrates limitations in explicitly modeling long-range dependency. Recently proposed hybrid CNN-Transformer architectures that combine the global perception capability of local feature and the local details of global reppresentations. However, the serial structure of CNN and transformer will increase the computational complexity, and then the redundant information generated by convolution operation may leads to the failure of long-range modeling. To this end, this paper proposes a double encoder framework including global encoder and local encoder, TransDE for short, to medical image segmentation. The global encoder takes transformer that designed for sequence-to-sequence prediction, while the local encoder adopts VGG-19 combined with the atrous spatial pyramid pooling (ASPP) to bring about local feature extraction. The experimental results of enteroscopy dataset and dermoscopy dataset show the superiority of our TransDE achieving around 1.97% improvement on CVC-ClinicDB in terms of DSC and 1.6% improvement on Lesion Boundary Segmentation challenge.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"142 1","pages":"374-378"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77360605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Fusion of Machine Learning for Teaching Case Research on Algorithm Course 融合机器学习的算法课程教学案例研究

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00120

Lisha Hu, Chunyu Hu

{"title":"Fusion of Machine Learning for Teaching Case Research on Algorithm Course","authors":"Lisha Hu, Chunyu Hu","doi":"10.1109/ITME53901.2021.00120","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00120","url":null,"abstract":"“Algorithm Design and Analysis” is a professional compulsory course for computer undergraduates. A solid grasp of the content of the course is of great importance for students to engage in relevant positions after graduation such as algorithm engineers or to further study. Nowadays, a large number of application problems need to be solved by machine learning algorithms. In fact, the underlying implementation details of many machine learning algorithms are also derived from these basic algorithms. However, there are few descriptions related to machine learning algorithms in current algorithm courses. In the process of teaching basic algorithms, if automatic association with machine learning algorithms can be realized, knowledge will root in the core knowledge base of students, thereby realizing continuous extension of knowledge. Based on this, this paper effectively associates divide and conquer and greedy algorithms with the machine learning representative --- decision tree algorithm, so as to improve the students' analogy of relevant contents and knowledge and draw inferences from one instance.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"56 1","pages":"569-572"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79452880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration and practice of “studio system” talent training mode of design specialty under the background of integration of industry and education 产教融合背景下设计专业“工作室制”人才培养模式的探索与实践

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00113

Xiaoyan Liu, Yang Xu

引用次数: 0

Analysis of psychological factors in irritable bowel syndrome 肠易激综合征的心理因素分析

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00088

Shejuan Liu, Zhimin Tao

引用次数: 0

The Evaluation System of SPOC Learning Engagement SPOC学习投入的评价体系

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00131

Lin Jinjiao, Zhao Yanze, Wen Yuhua, Gao Tianqi

引用次数: 0

An Improved Word Vector-Based Symptom Extraction Method for Traditional Chinese Medical Record Analysis 一种改进的基于词向量的病案症状提取方法

2021 11th International Conference on Information Technology in Medicine and Education (ITME) Pub Date : 2021-11-01 DOI: 10.1109/ITME53901.2021.00082

Zhongmin Liu, Zhiming Luo, Jiajun Xu, Shaozi Li

引用次数: 1