Pub Date : 2022-04-01DOI: 10.1109/pacificvis53943.2022.00019
Sanjana Srabanti, Michael Tran, Virginie Achim, David Fuller, Guadalupe Canahuate, Fabio Miranda, G Elisabeta Marai
The annual incidence of head and neck cancers (HNC) worldwide is more than 550,000 cases, with around 300,000 deaths each year. However, the incidence rates and disease-characteristics of HNC differ between treatment centers and different populations, due to undetermined reasons, which may or not include socioeconomic factors. The multi-faceted and multi-variate nature of the data in the context of the emerging field of health disparities research makes automated analysis impractical. Hence, we present a visual analysis approach to explore the health disparities in the data of HNC patients from two different cohorts at two cancer care centers. Our approach integrates data from multiple sources, including census data and city data, with custom visual encodings and with a nearest neighbor approach. Our design, created in collaboration with oncology experts, makes it possible to analyze the patients' demographic, disease characteristics, treatments and outcomes, and to make significant comparisons of these two cohorts and of individual patients. We evaluate this approach through two case studies performed with domain experts. The results demonstrate that this visual analysis approach successfully accomplishes the goal of comparing two cohorts in terms of different significant factors, and can provide insights into the main source of health disparities between the two centers.
{"title":"A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.","authors":"Sanjana Srabanti, Michael Tran, Virginie Achim, David Fuller, Guadalupe Canahuate, Fabio Miranda, G Elisabeta Marai","doi":"10.1109/pacificvis53943.2022.00019","DOIUrl":"https://doi.org/10.1109/pacificvis53943.2022.00019","url":null,"abstract":"<p><p>The annual incidence of head and neck cancers (HNC) worldwide is more than 550,000 cases, with around 300,000 deaths each year. However, the incidence rates and disease-characteristics of HNC differ between treatment centers and different populations, due to undetermined reasons, which may or not include socioeconomic factors. The multi-faceted and multi-variate nature of the data in the context of the emerging field of health disparities research makes automated analysis impractical. Hence, we present a visual analysis approach to explore the health disparities in the data of HNC patients from two different cohorts at two cancer care centers. Our approach integrates data from multiple sources, including census data and city data, with custom visual encodings and with a nearest neighbor approach. Our design, created in collaboration with oncology experts, makes it possible to analyze the patients' demographic, disease characteristics, treatments and outcomes, and to make significant comparisons of these two cohorts and of individual patients. We evaluate this approach through two case studies performed with domain experts. The results demonstrate that this visual analysis approach successfully accomplishes the goal of comparing two cohorts in terms of different significant factors, and can provide insights into the main source of health disparities between the two centers.</p>","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"2022 ","pages":"101-110"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344952/pdf/nihms-1822958.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9968895","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}
Pub Date : 2017-04-01DOI: 10.1109/PACIFICVIS.2017.8031571
G. Kim
The field of information visualization studies the interactive visual representations of data to reinforce human cognition, thereby facilitate the discovery of new tacit knowledge and even amplify human intelligence. Augmented reality (AR) shares the same objective and it can be treated as one particular form of information visualization where the data are both the real objects and the augmentations for them. As such, it presents a unique set of problems within the general requirements for an effective information visualization method. In this talk, I will first outline and put forth four main requirements for AR visualization, namely, (1) naturalness, (2) visibility (3) persistance/stability and (4) glass/hmd ergonomics. Then I will present a short survey of the existing AR visualization techniques and characterize them by their attributes and categorize them in terms of how they satisfy or address the proposed requirements. Finally, I will also introduce some of my own on-going research work in this area, specifically, for real time contrast adjustment for mobile augmented reality, usability with glasses, augmentation data organization, and multimodal AR data presentation. I hope that this work can instigate and shed some light on the future directions for further research in AR visualization techniques.
{"title":"Keynote speaker: Requirements and recent directions in augmented reality visualization","authors":"G. Kim","doi":"10.1109/PACIFICVIS.2017.8031571","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2017.8031571","url":null,"abstract":"The field of information visualization studies the interactive visual representations of data to reinforce human cognition, thereby facilitate the discovery of new tacit knowledge and even amplify human intelligence. Augmented reality (AR) shares the same objective and it can be treated as one particular form of information visualization where the data are both the real objects and the augmentations for them. As such, it presents a unique set of problems within the general requirements for an effective information visualization method. In this talk, I will first outline and put forth four main requirements for AR visualization, namely, (1) naturalness, (2) visibility (3) persistance/stability and (4) glass/hmd ergonomics. Then I will present a short survey of the existing AR visualization techniques and characterize them by their attributes and categorize them in terms of how they satisfy or address the proposed requirements. Finally, I will also introduce some of my own on-going research work in this area, specifically, for real time contrast adjustment for mobile augmented reality, usability with glasses, augmentation data organization, and multimodal AR data presentation. I hope that this work can instigate and shed some light on the future directions for further research in AR visualization techniques.","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"1 1","pages":"xiv"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/PACIFICVIS.2017.8031571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48764325","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}
Pub Date : 2017-04-01DOI: 10.1109/PACIFICVIS.2017.8031570
D. Ebert
To solve the world's challenges requires not only advancing computer science and big data analytics but requires new analysis and decision-making environments that effectively couple human decision making with advanced, guided analytics in a human-computer collaborative discourse and decision making (HCCD). While many researchers and companies are focusing solely on Big Data Analytics to harness the potential power in available massive, multisource, multivariate, evolving digital data, many of these big data solutions don't effectively factor the human decision maker into their proposed solution. The HCCD approach builds upon visual analytics and focuses on empowering the decision maker through interactive visual analytic environments where visual cognition, guided discovery, and non-digital human expertise and experience can be combined with state-of-the-art analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. This work and these systems can be applied to social change, advancing engineering, and science and solving some of the world's greatest challenges such as sustainability and security. In this talk, I'll outline this approach and highlight the potential and impact of application driven HCCD research.
{"title":"Keynote speaker: Changing the world with visual analytics","authors":"D. Ebert","doi":"10.1109/PACIFICVIS.2017.8031570","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2017.8031570","url":null,"abstract":"To solve the world's challenges requires not only advancing computer science and big data analytics but requires new analysis and decision-making environments that effectively couple human decision making with advanced, guided analytics in a human-computer collaborative discourse and decision making (HCCD). While many researchers and companies are focusing solely on Big Data Analytics to harness the potential power in available massive, multisource, multivariate, evolving digital data, many of these big data solutions don't effectively factor the human decision maker into their proposed solution. The HCCD approach builds upon visual analytics and focuses on empowering the decision maker through interactive visual analytic environments where visual cognition, guided discovery, and non-digital human expertise and experience can be combined with state-of-the-art analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. This work and these systems can be applied to social change, advancing engineering, and science and solving some of the world's greatest challenges such as sustainability and security. In this talk, I'll outline this approach and highlight the potential and impact of application driven HCCD research.","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"1 1","pages":"xiii"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/PACIFICVIS.2017.8031570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45080607","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}
Pub Date : 2015-04-01DOI: 10.1109/PACIFICVIS.2017.8031566
D. Weiskopf, Yingcai Wu, Tim Dwyer, Yun Jang, Naohisa Sakamoto
Welcome to the proceedings of the Pacific Visualization Symposium 2015 (PacificVis 2015), eighth in a series of successful events that have been sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee (VGTC). Past PacificVis symposia were held in Kyoto (2008), Beijing (2009), Taipei (2010), Hong Kong (2011), Songdo (2012), Sydney (2013), and Yokohama (2014). This year, PacificVis is held in China, organized by Zhejiang University, and held at Zijingang Campus, Zhejiang University, Hangzhou, China, from April 14th to 17th, 2015.
{"title":"Chair message","authors":"D. Weiskopf, Yingcai Wu, Tim Dwyer, Yun Jang, Naohisa Sakamoto","doi":"10.1109/PACIFICVIS.2017.8031566","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2017.8031566","url":null,"abstract":"Welcome to the proceedings of the Pacific Visualization Symposium 2015 (PacificVis 2015), eighth in a series of successful events that have been sponsored by the IEEE Computer Society Visualization and Graphics Technical Committee (VGTC). Past PacificVis symposia were held in Kyoto (2008), Beijing (2009), Taipei (2010), Hong Kong (2011), Songdo (2012), Sydney (2013), and Yokohama (2014). This year, PacificVis is held in China, organized by Zhejiang University, and held at Zijingang Campus, Zhejiang University, Hangzhou, China, from April 14th to 17th, 2015.","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"63 1","pages":"vii-viii"},"PeriodicalIF":0.0,"publicationDate":"2015-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84335101","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}
Pub Date : 2015-01-01DOI: 10.1109/PACIFICVIS.2015.7156348
Fei-Yue Wang
RSVP at www.uvu.edu/uwlp Gail Miller is the owner and Chair of the Board of the Larry H. Miller Group of Companies. The LHM Group is comprised of more than 80 companies, operating in 46 states and employing nearly 10,000 people. Gail has a legacy of giving back to the communities where the Group conducts business. This includes financial contributions, as well as her time and service. Gail and her husband Kim Wilson enjoy spending time with their combined nine children, thirty-four grandchildren and eight great-grandchildren.
{"title":"Keynote speaker","authors":"Fei-Yue Wang","doi":"10.1109/PACIFICVIS.2015.7156348","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2015.7156348","url":null,"abstract":"RSVP at www.uvu.edu/uwlp Gail Miller is the owner and Chair of the Board of the Larry H. Miller Group of Companies. The LHM Group is comprised of more than 80 companies, operating in 46 states and employing nearly 10,000 people. Gail has a legacy of giving back to the communities where the Group conducts business. This includes financial contributions, as well as her time and service. Gail and her husband Kim Wilson enjoy spending time with their combined nine children, thirty-four grandchildren and eight great-grandchildren.","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"4 1","pages":"xiii"},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81910957","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}
Pub Date : 2012-12-31DOI: 10.1109/PacificVis.2012.6183591
Fangxiang Jiao, Jeff M Phillips, Yaniv Gur, Chris R Johnson
In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call SIP functions of diffusion shapes, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.
{"title":"Uncertainty Visualization in HARDI based on Ensembles of ODFs.","authors":"Fangxiang Jiao, Jeff M Phillips, Yaniv Gur, Chris R Johnson","doi":"10.1109/PacificVis.2012.6183591","DOIUrl":"https://doi.org/10.1109/PacificVis.2012.6183591","url":null,"abstract":"<p><p>In this paper, we propose a new and accurate technique for uncertainty analysis and uncertainty visualization based on fiber orientation distribution function (ODF) glyphs, associated with high angular resolution diffusion imaging (HARDI). Our visualization applies volume rendering techniques to an ensemble of 3D ODF glyphs, which we call <i>SIP functions</i> of <i>diffusion shapes</i>, to capture their variability due to underlying uncertainty. This rendering elucidates the complex heteroscedastic structural variation in these shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the <i>certain volume ratio</i>. Our uncertainty analysis and visualization framework is then applied to synthetic data, as well as to HARDI human-brain data, to study the impact of various image acquisition parameters and background noise levels on the diffusion shapes.</p>","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"2013 ","pages":"193-200"},"PeriodicalIF":0.0,"publicationDate":"2012-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/PacificVis.2012.6183591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32065559","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}
Pub Date : 2012-02-28DOI: 10.1109/PacificVis.2012.6183567
C. Bajaj
Discoveries in computational molecular - cell biology and bioinformatics promise to provide new therapeutic interventions to disease. With the rapid growth of sequence and structural information for thousands of proteins and hundreds of cell types, computational processing are a restricting factor in obtaining quantitative understanding of molecular-cellular function. Processing and analysis is necessary both for input data (often from imaging) and simulation results. To make biological conclusions, this data must be input to and combined with results from computational analysis and simulations. Furthermore, as parallelism is increasingly prevalent, utilizing the available processing power is essential to development of scalable solutions needed for realistic scientific inquiry. However, complex image processing and even simulations performed on large clusters, multi-core CPU, GPU-type parallelization means that naive cache unaware algorithms may not efficiently utilize available hardware. Future gains thus require improvements to a core suite of algorithms underpinning the data processing, simulation, optimization and visualization needed for scientific discovery. In this talk, I shall highlight current progress on these algorithms as well as provide several challenges for the visualization community.
{"title":"Quantitative visualization in the computational biological sciences","authors":"C. Bajaj","doi":"10.1109/PacificVis.2012.6183567","DOIUrl":"https://doi.org/10.1109/PacificVis.2012.6183567","url":null,"abstract":"Discoveries in computational molecular - cell biology and bioinformatics promise to provide new therapeutic interventions to disease. With the rapid growth of sequence and structural information for thousands of proteins and hundreds of cell types, computational processing are a restricting factor in obtaining quantitative understanding of molecular-cellular function. Processing and analysis is necessary both for input data (often from imaging) and simulation results. To make biological conclusions, this data must be input to and combined with results from computational analysis and simulations. Furthermore, as parallelism is increasingly prevalent, utilizing the available processing power is essential to development of scalable solutions needed for realistic scientific inquiry. However, complex image processing and even simulations performed on large clusters, multi-core CPU, GPU-type parallelization means that naive cache unaware algorithms may not efficiently utilize available hardware. Future gains thus require improvements to a core suite of algorithms underpinning the data processing, simulation, optimization and visualization needed for scientific discovery. In this talk, I shall highlight current progress on these algorithms as well as provide several challenges for the visualization community.","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2012-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398167","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}
Pub Date : 2012-01-01DOI: 10.1109/pacificvis.2012.6183592
Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen
2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.
{"title":"FluoRender: An Application of 2D Image Space Methods for 3D and 4D Confocal Microscopy Data Visualization in Neurobiology Research.","authors":"Yong Wan, Hideo Otsuna, Chi-Bin Chien, Charles Hansen","doi":"10.1109/pacificvis.2012.6183592","DOIUrl":"10.1109/pacificvis.2012.6183592","url":null,"abstract":"<p><p>2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.</p>","PeriodicalId":73302,"journal":{"name":"IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium","volume":" ","pages":"201-208"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3622106/pdf/nihms370292.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31357547","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}
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