Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology最新文献

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Acceleration of Two Point Correlation Function Calculation for Pathology Image Segmentation. 用于病理学图像分割的两点相关函数计算的加速。
Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology Pub Date : 2011-07-01 Epub Date: 2011-10-27 DOI: 10.1109/HISB.2011.10
Lee A D Cooper, Joel H Saltz, Umit Catalyurek, Kun Huang
{"title":"Acceleration of Two Point Correlation Function Calculation for Pathology Image Segmentation.","authors":"Lee A D Cooper, Joel H Saltz, Umit Catalyurek, Kun Huang","doi":"10.1109/HISB.2011.10","DOIUrl":"10.1109/HISB.2011.10","url":null,"abstract":"The segmentation of tissue regions in high-resolution microscopy is a challenging problem due to both the size and appearance of digitized pathology sections. The two point correlation function (TPCF) has proved to be an effective feature to address the textural appearance of tissues. However the calculation of the TPCF functions is computationally burdensome and often intractable in the gigapixel images produced by slide scanning devices for pathology application. In this paper we present several approaches for accelerating deterministic calculation of point correlation functions using theory to reduce computation, parallelization on distributed systems, and parallelization on graphics processors. Previously we show that the correlation updating method of calculation offers an 8-35x speedup over frequency domain methods and decouples efficient computation from the select scales of Fourier methods. In this paper, using distributed computation on 64 compute nodes provides a further 42x speedup. Finally, parallelization on graphics processors (GPU) results in an additional 11-16x speedup using an implementation capable of running on a single desktop machine.","PeriodicalId":91600,"journal":{"name":"Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"2011 ","pages":"174-181"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/HISB.2011.10","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36314508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Applying an Instance-specific Model to Longitudinal Clinical Data for Prediction. 将实例特定模型应用于纵向临床数据预测。
Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology Pub Date : 2011-07-01 Epub Date: 2011-10-27 DOI: 10.1109/HISB.2011.12
Emily Watt, James W Sayre, Alex A T Bui
{"title":"Applying an Instance-specific Model to Longitudinal Clinical Data for Prediction.","authors":"Emily Watt,&nbsp;James W Sayre,&nbsp;Alex A T Bui","doi":"10.1109/HISB.2011.12","DOIUrl":"https://doi.org/10.1109/HISB.2011.12","url":null,"abstract":"<p><p>Dynamic Bayesian Belief networks (DBNs) have been commonly used to represent temporal data in several domains; however, an ideal representation requires a near perfect mapping between the process being modeled and the DBN. Furthermore, DBNs assume a full set of observations collected at a fixed frequency. Bayesian model selection has arisen to address biased inference and underlying assumptions about the data (e.g., distribution, representativeness) to choose a model that best fits the given observations. Per patient case, a Bayesian model is generated to maximize specificity, and the collective set of models is averaged to fit all examples. This paper demonstrates the advantages of patient-specific modeling over a DBN-driven approach. Results evaluating this approach are presented based on models for two longitudinal clinical datasets (neuro-oncology, knee osteoarthritis). Largely, the patient-specific models show improved performance in prediction relative to the DBNs.</p>","PeriodicalId":91600,"journal":{"name":"Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"2011 ","pages":"81-88"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/HISB.2011.12","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34343984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Case-based Retrieval System using Natural Language Processing and Population-based Visualization. 基于自然语言处理和群体可视化的案例检索系统。
William Hsu, Ricky K Taira, Fernando Viñuela, Alex A T Bui
{"title":"A Case-based Retrieval System using Natural Language Processing and Population-based Visualization.","authors":"William Hsu,&nbsp;Ricky K Taira,&nbsp;Fernando Viñuela,&nbsp;Alex A T Bui","doi":"10.1109/HISB.2011.3","DOIUrl":"https://doi.org/10.1109/HISB.2011.3","url":null,"abstract":"<p><p>Electronic medical records capture large quantities of patient data generated as a result of routine care. Secondary use of this data for clinical research could provide new insights into the evolution of diseases and help assess the effectiveness of available interventions. Unfortunately, the unstructured nature of clinical data hinders a user's ability to understand this data: tools are needed to structure, model, and visualize the data to elucidate patterns in a patient population. We present a case-based retrieval framework that incorporates an extraction tool to identify concepts from clinical reports, a disease model to capture necessary context for interpreting extracted concepts, and a model-driven visualization to facilitate querying and interpretation of the results. We describe how the model is used to group, filter, and retrieve similar cases. We present an application of the framework that aids users in exploring a population of intracranial aneurysm patients.</p>","PeriodicalId":91600,"journal":{"name":"Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology","volume":"2011 ","pages":"221-228"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/HISB.2011.3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34343987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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