Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences最新文献_第5页

Review of the Application of Blockchain Technology in Traditional Chinese Medicine Field 区块链技术在中医药领域的应用综述

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429932

Shirui Zhou, Hui Sheng, Jingang Ma, Xiaochun Han

引用次数: 2

Two-step Content-based Retrieval for Pulmonary Nodule Diagnosis 基于两步内容的肺结节诊断检索

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429934

Chune Li, Jingang Ma, Guohui Wei

{"title":"Two-step Content-based Retrieval for Pulmonary Nodule Diagnosis","authors":"Chune Li, Jingang Ma, Guohui Wei","doi":"10.1145/3429889.3429934","DOIUrl":"https://doi.org/10.1145/3429889.3429934","url":null,"abstract":"Similarity measurement of pulmonary nodules can be useful in content-based retrieval for pulmonary nodule diagnosis on computed tomography (CT). Unlike previous retrieval schemes, which concentrate on the feature extracting, we focus on the similarity measurement of pulmonary nodules. Similar to our previous studies, in this study, the pulmonary nodule dataset is from the LIDC-IDRI lung CT images, which includes 746 pulmonary nodules, 375 malignant nodules and 371 benign nodules. Each nodule is represented by a vector of 26 texture features. Two-step similarity measurement is proposed to construct a content-based image retrieval (CBIR) scheme to discriminant benign and malignant nodules. The similarities of pulmonary nodules are defined as semantic relevance and visual similarity. In the first step, semantic relevance is used to screen the nodules, which are semantic relevance to the query nodule. For the second step, visual similarity is applied to calculate the nodules, which look like the query nodules. Two Mahalanobis distances are learned to preserve semantic relevance and visual similarity of lung nodules, respectively. A retrieval scheme applies the learned Mahalanobis distances to calculate the similar nodules. Classification accuracy is used to evaluate the scheme performance, the area under the ROC curve (AUC) can reach 0.956±0.005.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124943518","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

Ras mutations in the Erk-MAPK pathway and Cancer: Alternate therapies examined in an ODE-based computational model Erk-MAPK通路中的Ras突变与癌症:在基于ode的计算模型中检查的替代疗法

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429891

Jiarui Li

{"title":"Ras mutations in the Erk-MAPK pathway and Cancer: Alternate therapies examined in an ODE-based computational model","authors":"Jiarui Li","doi":"10.1145/3429889.3429891","DOIUrl":"https://doi.org/10.1145/3429889.3429891","url":null,"abstract":"Oncogenic mutations in Ras GTPases result in overactivation of the Erk-MAPK pathway, which regulate cellular proliferation, and have been shown to have strong clinical correlations with a variety of pancreatic, colonic, and other cancers. To date, a viable inhibitor of Ras has remained elusive and most Ras mutation cancers are treated with downstream inhibitors to reduce the activity of ERK. However, these therapies have been shown to provide minor anticancer effects in some patients. To examine other viable therapeutic alternatives, potential drug actions that activate \"off\" mechanisms of this pathway are examined in a computational model. The described model recapitulates empirical data of the Erk-MAPK pathway under EGF-stimulation and is modified to include the effects of a constitutively active Ras. Serving as an experimental predictor of the potential drug actions against the effects of constant Ras activation, activators of protein phosphatase 2 (PP2A) and Raf1 phosphatase catalytic activity are shown to be able to restore Erk responses in such cancers to dynamic responses that are seen in wild-type EGF-stimulated cells.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126252303","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

Automatic retinal structure segmentation via U-Net framework and quantitative analysis with ImageJ 基于U-Net框架的视网膜结构自动分割与ImageJ的定量分析

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429908

Shenghui Zhao, Weizheng Kong, Jiahui Shao, Zicheng Zhou, Wen Chen, Huiqun Wu

{"title":"Automatic retinal structure segmentation via U-Net framework and quantitative analysis with ImageJ","authors":"Shenghui Zhao, Weizheng Kong, Jiahui Shao, Zicheng Zhou, Wen Chen, Huiqun Wu","doi":"10.1145/3429889.3429908","DOIUrl":"https://doi.org/10.1145/3429889.3429908","url":null,"abstract":"Retina is a window reflecting the state of the general system and the condition of the eye. The traditional heavy-manual quantification of the morphological characteristics of retina is in need of improving. Therefore, we aim to automatically segment the retinal vessels and lesions and further analyze segmented results. Three public datasets including DRIVE, DIARETDB, IDRID were selected, and the images from them were preprocessed and augmented with a series of enhancements, random rotation and gamma transformation. Attention gate (AG) U-Net framework was used to segment retinal vessels, OD and exudates respectively. The performance of AG U-Net model was evaluated. Furthermore, the segmented results were analyzed with different shape descriptors using ImageJ. The geometric features such as width, length, area, circumference, and roundness were extracted and statistically analyzed in grade 2 and 3 DR images. The results approved the superiority of Attention-U-Net in retinal structure segmentation and the geometric features were successfully extracted and analyzed. In conclusion, the proposed AG U-Net empowered automatic segmentation and ImageJ based analytic framework are worthy of applications on retinal images, thus fostering the clinical science investigations.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682672","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

Immunotherapies For Cancer, a Promising Cure? 免疫疗法治疗癌症，一个有希望的治愈方法?

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429942

Xinlan Yang

{"title":"Immunotherapies For Cancer, a Promising Cure?","authors":"Xinlan Yang","doi":"10.1145/3429889.3429942","DOIUrl":"https://doi.org/10.1145/3429889.3429942","url":null,"abstract":"Cancer is a common cause of death around the world. To date, surgery is still the only curative option for most types of cancer. Moreover, the majority of cancer is a prominent resistance to traditional therapies that we have long been using, including chemotherapy and radiotherapy. In recent years, different types of immune cells have been recognized as a critical component in therapies. Especially, the cancer-immunotherapy has come into the spotlight. In a number of clinical trials, it has shown capabilities of addressing the defects of transitional therapies, achieving complete eradication of neoplasms and constructing a long-lasting immunity to prevent recurrence. Currently, remarkable progress and innovations in methods and approaches are made. In this article, we first discuss the main types of immune cells participated in anti-tumor/cancer activities, immunotherapy and its applications in multiple fields. Next, we summarize the associations between the immune system and cancer, and current immunotherapies for cancer, including specific examples or experimental trials, with the advantages and disadvantages of each. Despite many unsolved questions regarding immunotherapy such as financial concerns, the current paper overall demonstrates that the development of immunotherapy is an emerging and potentially influential therapy for improving the survival rate and prognosis of cancer.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463781","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

Retinal Blood Vessel Segmentation via Attention Gate Network 基于注意门网络的视网膜血管分割

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429936

Kaiqi Li, Zeyi Yao, Yiwen Luo, Xingqun Qi, Pengkun Liu, Zijian Wang

引用次数: 1

Prediction of 2019-nCoV Epidemic by Linear Regression Model 基于线性回归模型的新型冠状病毒流行预测

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429890

Ziye Hong

引用次数: 1

Synthesizing Missing Data using 3D Reversible GAN for Alzheimer's Disease 利用三维可逆GAN合成阿尔茨海默病缺失数据

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429929

Wanyun Lin

引用次数: 5

Automated detection of atrial fibrillation based on DenseNet using ECG signals 基于DenseNet的心电信号心房颤动自动检测

Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences Pub Date : 2020-09-11 DOI: 10.1145/3429889.3429902

Xingxiang Tao, Hao Dang, Danqun Xiong, Ruiqing Liu, Dongjie Liu, Fulin Zhou

{"title":"Automated detection of atrial fibrillation based on DenseNet using ECG signals","authors":"Xingxiang Tao, Hao Dang, Danqun Xiong, Ruiqing Liu, Dongjie Liu, Fulin Zhou","doi":"10.1145/3429889.3429902","DOIUrl":"https://doi.org/10.1145/3429889.3429902","url":null,"abstract":"Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it can cause a variety of cardiovascular diseases. Nonetheless, the early stage of AF is usually paroxysmal, with strong concealment. Electrocardiogram (ECG) is one of the most important noninvasive diagnostic tools for heart disease. However, in order to interpret ECG accurately, clinicians need to have well-trained professional knowledge and skills. It is valuable to develop an efficient, accurate and stable automatic AF detection algorithm in clinical settings. In this paper, we propose a novel network architecture, named DenseNet-BLSTM network model, for automatically AF detection using the ECG signals. The proposed model is constructed integrating the DenseNet module, the BLSTM module, two fully connected layers and one SoftMax layer. In this paper, the DenseNet module is utilized for further capturing local feature maps, whereas the BLSTM module is used to obtain the long-term dependencies in ECG signals. The datasets used to validate and test the proposed model are from the MIT-BIH Atrial Fibrillation Database (MIT-AF). The experimental results show that our proposed model achieved 99.07% and 98.15% accuracy in training and validation set, and achieved 97.78% accuracy in the testing set which is unseen dataset. The proposed DenseNet-BLSTM has shown excellent robustness and accuracy in automatic AF detection.","PeriodicalId":315899,"journal":{"name":"Proceedings of the 1st International Symposium on Artificial Intelligence in Medical Sciences","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126987815","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: 3