2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)最新文献_第3页

Label the many with a few: Semi-automatic medical image modality discovery in a large image collection 用少数标记多:在大型图像集合中半自动医学图像模态发现

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007850

Szilárd Vajda, D. You, Sameer Kiran Antani, G. Thoma

{"title":"Label the many with a few: Semi-automatic medical image modality discovery in a large image collection","authors":"Szilárd Vajda, D. You, Sameer Kiran Antani, G. Thoma","doi":"10.1109/CICARE.2014.7007850","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007850","url":null,"abstract":"In this paper we present a fast and effective method for labeling images in a large image collection. Image modality detection has been of research interest for querying multimodal medical documents. To accurately predict the different image modalities using complex visual and textual features, we need advanced classification schemes with supervised learning mechanisms and accurate training labels. Our proposed method, on the other hand, uses a multiview-approach and requires minimal expert knowledge to semi-automatically label the images. The images are first projected in different feature spaces, and are then clustered in an unsupervised manner. Only the cluster representative images are labeled by an expert. Other images from the cluster “inherit” the labels from these cluster representatives. The final label assigned to each image is based on a voting mechanism, where each vote is derived from different feature space clustering. Through experiments we show that using only 0.3% of the labels was sufficient to annotate 300,000 medical images with 49.95% accuracy. Although, automatic labeling is not as precise as manual, it saves approximately 700 hours of manual expert labeling, and may be sufficient for next-stage classifier training. We find that for this collection accuracy improvements are feasible with better disparate feature selection or different filtering mechanisms.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115759886","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

Identifying risk factors associate with hypoglycemic events 确定与低血糖事件相关的危险因素

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007851

R. Duan, H. Fu, Chenchen Yu

引用次数: 0

Cognitively inspired speech processing for multimodal hearing technology 多模态听力技术的认知启发语音处理

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007834

Andrew Abel, A. Hussain, B. Luo

{"title":"Cognitively inspired speech processing for multimodal hearing technology","authors":"Andrew Abel, A. Hussain, B. Luo","doi":"10.1109/CICARE.2014.7007834","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007834","url":null,"abstract":"In recent years, the link between the various human communication production domains has become more widely utilised in the field of speech processing. Work by the authors and others has demonstrated that intelligently integrated audio and visual information can be used for speech enhancement. This advance in technology means that the use of visual information as part of hearing aids or assistive listening devices is becoming ever more viable. One issue that is not commonly explored is how a multimodal system copes with variations in data quality and availability, such as a speaker covering their face while talking, or the existence of multiple speakers in a conversational scenario, an issue that a hearing device would be expected to cope with by switching between different programmes and settings to adapt to changes in the environment. We present the ChallengAV audiovisual corpus, which is used to evaluate a novel fuzzy logic based audiovisual switching system, designed to be used as part of a next-generation adaptive, autonomous, context aware hearing system. Initial results show that the detectors are capable of determining environmental conditions and responding appropriately, demonstrating the potential of such an adaptive multimodal system as part of a state of the art hearing aid device.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"20 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120995313","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: 5

Towards a Personal Health Records system for patients with Autism Spectrum Disorders 面向自闭症谱系障碍患者的个人健康记录系统

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007839

A. Coronato, Giovanni Paragliola

引用次数: 4

A novel mixed values k-prototypes algorithm with application to health care databases mining 一种新的混合值k-原型算法及其在医疗保健数据库挖掘中的应用

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 2014-12-01 DOI: 10.1109/CICARE.2014.7007849

Ahmed Najjar, Christian Gagné, D. Reinharz

引用次数: 5

Privacy preservation, sharing and collection of patient records using cryptographic techniques for cross-clinical secondary analytics 使用加密技术进行跨临床二级分析的隐私保护、共享和收集患者记录

2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) Pub Date : 1900-01-01 DOI: 10.1109/CICARE.2014.7007847

H. Abdulrahman, N. Poh, J. Burnett

{"title":"Privacy preservation, sharing and collection of patient records using cryptographic techniques for cross-clinical secondary analytics","authors":"H. Abdulrahman, N. Poh, J. Burnett","doi":"10.1109/CICARE.2014.7007847","DOIUrl":"https://doi.org/10.1109/CICARE.2014.7007847","url":null,"abstract":"The growing interest in research on Clinical Medical Records (CMRs) presents opportunities in finding meaningful patterns of symptoms, treatments and patient outcomes. The typically distributed collection of CMRs across various clinical centres suggests the need to integrate the records in a centralized data repository. This is necessary to explore many data analytic algorithms which are not supported on distributed databases. As highly private patient records are being dealt with, it is important to consider how privacy will be preserved. This is especially important since the patient records are to be shared and used for reasons other than the primary reasons they were collected, i.e., for secondary use of healthcare data. In addition, the need for securing data transmission becomes necessary to ensure privacy and confidentiality. We advance the literature on privacy-enhancing data minining in the healthcare setting by (1) presenting strategies of using de-identification as well as cryptographic techniques to facilitate patient identity protection and securely transmit the records to a centralized data repository for secondary data analytics; (2) addressing key management issues related to the use of cryptography constructs; and (3) establishing the security requirements as well as carrying out vulnerability assessment with respect to the tranmission process, data repository, and direct attacks to the encrypted patient ID.","PeriodicalId":120730,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184316","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