Visual Informatics最新文献

Advances in AI-powered photorealistic ultrasound visualization techniques: A survey 人工智能驱动的逼真超声可视化技术进展综述

IF 4.4 3区计算机科学

Visual Informatics Pub Date : 2026-09-01 Epub Date: 2026-04-08 DOI: 10.1016/j.visinf.2026.100323

Chenyang Zhao, Zhuofan Deng, Qiong Zeng, Fan Zhong, Lin Lu, Wenhua Zhang, Changhe Tu

{"title":"Advances in AI-powered photorealistic ultrasound visualization techniques: A survey","authors":"Chenyang Zhao, Zhuofan Deng, Qiong Zeng, Fan Zhong, Lin Lu, Wenhua Zhang, Changhe Tu","doi":"10.1016/j.visinf.2026.100323","DOIUrl":"10.1016/j.visinf.2026.100323","url":null,"abstract":"<div><div>Ultrasound data visualization is essential for diagnosis, anomaly detection, and doctor-patient communication. In recent years, real-time 3D ultrasound and rendering technologies have matured, and leading international ultrasound vendors such as GE Healthcare and Philips have incorporated realistic rendering techniques into ultrasound data visualization, achieving high image quality critical for both diagnostic and display purposes. However, due to the inherently low signal-to-noise ratio and occlusions in ultrasound data, generating highly realistic visualizations remains challenging. Artificial intelligence (AI) offers new opportunities to enhance ultrasound visualization by improving data reconstruction quality, speckle noise reduction, and physically based rendering. In this survey, we review AI-powered approaches across the ultrasound visualization pipeline, building on traditional techniques in data reconstruction, data preprocessing, and realistic rendering, highlighting how AI techniques address the unique characteristics of ultrasound data. Finally, we discuss emerging trends and future directions toward increasingly lifelike and intelligent ultrasound visualization.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 3","pages":"Article 100323"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148940909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A visualization framework for hierarchical structure enhanced mind mapping 层次结构的可视化框架增强了思维导图

IF 4.4 3区计算机科学

Visual Informatics Pub Date : 2026-09-01 Epub Date: 2026-05-06 DOI: 10.1016/j.visinf.2026.100325

Yang Liu, Heyu Wang, Lu Wei, Wei Chen, Yuanxiang Ji

引用次数: 0

A visual analytics approach to piecewise linear regression modeling for flow characteristic curve correction 流量特性曲线修正分段线性回归模型的可视化分析方法

IF 4.4 3区计算机科学

Visual Informatics Pub Date : 2026-09-01 Epub Date: 2026-05-27 DOI: 10.1016/j.visinf.2026.100322

Lianen Ji, Xinyuan Zhang, Huikai Wang, Wenbo Wang, Xi Zhang, Jianfeng Wang

{"title":"A visual analytics approach to piecewise linear regression modeling for flow characteristic curve correction","authors":"Lianen Ji, Xinyuan Zhang, Huikai Wang, Wenbo Wang, Xi Zhang, Jianfeng Wang","doi":"10.1016/j.visinf.2026.100322","DOIUrl":"10.1016/j.visinf.2026.100322","url":null,"abstract":"<div><div>In large-scale process industries, such as thermal power plants, regularly correcting the flow characteristic curve of the main valves using historical data is essential for ensuring accurate flow control. Piecewise linear regression modeling is a practical and effective method for this correction. However, the large size of historical data, the uneven sampling distribution, and the complexity of the optimization process often lead to serious problems, such as long iteration cycles and low modeling quality in existing practices. Regarding these issues in the industrial applications, this paper presents a visual analytics approach to improve flow characteristic curve correction from three perspectives: (a) interactively extracting flow characteristic data from vast historical samples with long periods and multidimensional correlation; (b) iteratively optimizing the model resolution process through data segmentation and heuristic algorithms in multiple aspects; (c) comprehensively evaluating, comparing, and fusing multiple candidate models. On this basis, we developed an interactive visualization tool, PlReVA, to integrate user interaction and domain knowledge deeply into the modeling process. A real-world case study using this tool was demonstrated on the historical dataset of actual thermal power generation. The effectiveness and practicality of PlReVA were confirmed through quantitative evaluation and feedback from domain experts.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 3","pages":"Article 100322"},"PeriodicalIF":4.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148940908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CovidDashboardWild: An interactive review on COVID-19 visualization dashboards in the wild covid - dashboardwild：对COVID-19可视化仪表板的交互式审查

IF 4.4 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2025-10-28 DOI: 10.1016/j.visinf.2025.100287

Guijuan Wang, Yadong Wu, Jiaxin Yang, Han Su, Ziyi Tang, Changwei Luo, Lu Tong, Xiaorong Zhang, Wenjuan Bai, Lijuan Peng

{"title":"CovidDashboardWild: An interactive review on COVID-19 visualization dashboards in the wild","authors":"Guijuan Wang, Yadong Wu, Jiaxin Yang, Han Su, Ziyi Tang, Changwei Luo, Lu Tong, Xiaorong Zhang, Wenjuan Bai, Lijuan Peng","doi":"10.1016/j.visinf.2025.100287","DOIUrl":"10.1016/j.visinf.2025.100287","url":null,"abstract":"<div><div>Since the global outbreak of COVID-19, numerous interactive and dynamically updated dashboards have been deployed worldwide to disseminate critical information to broad audiences. Unlike traditional researcher-led and lab-based visualization studies, these dashboards represent one of the first large-scale, real-world applications of visualization technology to address a global crisis, offering unique insights for the visualization community into how visual systems are adopted in practice. However, these dashboards are gradually disappearing from the internet as the pandemic subsides. To document these historically significant practices, while responding to the visualization community’s call for practice-driven research, we consolidate and review a dataset of 208 COVID-19 dashboards deployed in real-world settings (“in the wild”). We develop a nested hierarchical taxonomy spanning four levels, 12 named categories, and over 70 exploratory tags. Using this taxonomy, we systematically analyze the dashboards to identify their unique characteristics and design space. We conclude with research challenges and future directions. To support further exploration, we provide an interactive browser (available at <span><span>https://pandemicdashboards.github.io/</span><svg><path></path></svg></span>) that catalogs the reviewed dashboards with their design attributes and archived snapshots.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 2","pages":"Article 100287"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148553660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Face-aware Roman mosaic rendering for an interactive museum kiosk 交互式博物馆亭的面部感知罗马马赛克渲染

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2025-09-23 DOI: 10.1016/j.visinf.2025.100282

Beatriz Madeira , Jorge C.S. Cardoso

{"title":"Face-aware Roman mosaic rendering for an interactive museum kiosk","authors":"Beatriz Madeira , Jorge C.S. Cardoso","doi":"10.1016/j.visinf.2025.100282","DOIUrl":"10.1016/j.visinf.2025.100282","url":null,"abstract":"<div><div>This paper describes the development and evaluation of an interactive kiosk that enables museum visitors to create personalised Roman mosaics. The kiosk uses a novel algorithm, an extension of Battiato’s algorithm, which integrates advanced face detection technology to render digital mosaics from real-time photographs. This system aims to merge the historical significance of Roman mosaics with contemporary interactive technologies, making ancient art more accessible to the public.</div><div>We assessed the execution and visual results of the algorithm and evaluated the kiosk in both academic and museum environments, where it was assessed for user interaction, aesthetic appeal, and operational performance. The findings indicate that while the kiosk successfully engages visitors, it faces challenges such as excessive processing times and reliance on continuous internet connectivity. These issues highlight areas for potential improvement in both the algorithm’s efficiency and the kiosk’s physical design to better suit museum settings.</div><div>Overall, this study contributes a new mosaic rendering algorithm to the field and provides insights into its application in enhancing experiences in cultural heritage settings. The results offer directions for future research and development in the deployment of interactive technologies in museums.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 2","pages":"Article 100282"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148185686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RankLens: Visual analysis of evolving rankings in multiple network RankLens：对多个网络中不断变化的排名进行可视化分析

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2025-12-27 DOI: 10.1016/j.visinf.2025.100304

Yi Ding , Fengze Yu , Bangting Xu , Xiangyu Zhu , Xiaogang Guo , Songyue Li

引用次数: 0

Sharper insights: Discrimination-enhanced semantic segmentation of similar morphology nuclei in breast cancer pathology images 更清晰的见解：乳腺癌病理图像中相似形态核的区分增强语义分割

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2025-11-19 DOI: 10.1016/j.visinf.2025.100289

Rui Yang , Guoxin Sun , Yunfei Bai , Yuehua Liu , Liming Xin , Xiao Zou , Shaorong Xie

{"title":"Sharper insights: Discrimination-enhanced semantic segmentation of similar morphology nuclei in breast cancer pathology images","authors":"Rui Yang , Guoxin Sun , Yunfei Bai , Yuehua Liu , Liming Xin , Xiao Zou , Shaorong Xie","doi":"10.1016/j.visinf.2025.100289","DOIUrl":"10.1016/j.visinf.2025.100289","url":null,"abstract":"<div><div>Similar Morphology Cells (SMCs) play a critical role in pathology diagnosis. This study identifies and defines two primary challenges for SMCs segmentation based on a detailed investigation. At the macro level of slides, the sample distribution within pathology images is highly imbalanced due to varying cell functionalities. At the micro level of cells, differences between SMCs are imperceptible. These two challenges lead to confusion between visually similar cell classes in SMCs segmentation, reducing segmentation accuracy. To address these issues, we propose Sharper Insights (SI), a novel approach designed to enhance the model’s discriminative capabilities. Specifically, we introduce a Discrimination Enhancement Module (DEM) to increase the model’s sensitivity to subtle variations among SMCs. This module utilizes two attention mechanisms to sharpen the model’s focus on minute differences. Additionally, a novel loss function, Minority Categories Augmentation (MCA), is introduced to mitigate distribution imbalance by dynamically adjusting category-specific weights. Finally, to verify the effectiveness and generalization of the proposed method SI, we curate a comprehensive breast cancer dataset SimMorphBC. It primarily focuses on SMCs, which have not been explicitly proposed in previous datasets. Extensive experiments on breast cancer pathology images demonstrate the superior performance of the proposed approach.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 2","pages":"Article 100289"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148185687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatially-anchored visual diagnostics for smart construction 智能建筑的空间锚定视觉诊断

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2026-06-05 DOI: 10.1016/j.visinf.2026.100334

Erqing Zhang, Longting Zhu, Minfeng Zhu, Wei Chen

引用次数: 0

Enhancing spatial learning under visual cues: A comparative study of virtual environments and perspectives 视觉提示下增强空间学习：虚拟环境与视角的比较研究

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2026-01-12 DOI: 10.1016/j.visinf.2025.100301

Yifei Zhang , Lizhe Chen , Jun-Hsiang Yao , Zhan Wang , Siming Chen

{"title":"Enhancing spatial learning under visual cues: A comparative study of virtual environments and perspectives","authors":"Yifei Zhang , Lizhe Chen , Jun-Hsiang Yao , Zhan Wang , Siming Chen","doi":"10.1016/j.visinf.2025.100301","DOIUrl":"10.1016/j.visinf.2025.100301","url":null,"abstract":"<div><div>Spatial learning is a crucial cognitive process through which individuals acquire and retain information about their environment, playing a vital role in real-world applications such as emergency training and urban planning. Recent research has increasingly focused on virtual environments, where visualizations such as path encoding can significantly aid in understanding spatial knowledge, such as escape routes in emergency situations. To explore how different virtual environments and perspectives enhance spatial learning under visualization, this study investigated two different virtual environments — desktop and Virtual Reality (VR) — and the impacts of first-person and third-person perspectives. We developed a prototype based on expert interviews and real-world needs, conducting a between-subjects study involving 48 participants. The experimental design systematically varied the virtual environments and perspectives to assess their effects on participants’ performance of spatial learning under the same visual cues. The focus was on three types of spatial knowledge: landmark knowledge, route knowledge, and survey knowledge. The results indicate that, under the same virtual environment, the third-person perspective is more effective in enhancing survey knowledge, while the first-person perspective is better suited for acquiring landmark knowledge. Furthermore, using the third-person perspective in VR environments and the first-person perspective in desktop environments yields better spatial learning outcomes compared to other conditions.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 2","pages":"Article 100301"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148185688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evolutionary design of a visual analytics interface to study predictive patterns in high dimensional data 一个可视化分析界面的进化设计，用于研究高维数据中的预测模式

IF 3.8 3区计算机科学

Visual Informatics Pub Date : 2026-06-01 Epub Date: 2025-12-24 DOI: 10.1016/j.visinf.2025.100303

Darius Coelho, Eric Papenhausen, Klaus Mueller

{"title":"Evolutionary design of a visual analytics interface to study predictive patterns in high dimensional data","authors":"Darius Coelho, Eric Papenhausen, Klaus Mueller","doi":"10.1016/j.visinf.2025.100303","DOIUrl":"10.1016/j.visinf.2025.100303","url":null,"abstract":"<div><div>Organizations increasingly rely on data mining to discover predictive patterns that inform decision-making. However, the vast number of patterns produced by mining algorithms, especially in high-dimensional datasets, can overwhelm analysts. To address this challenge, we present the evolutionary design of a visual analytics interface that enables users to explore and interpret mined predictive patterns effectively. Starting from a projection-based prototype rooted in research tools, we iteratively refined the interface through feedback from data analysts and real-world usage. Each design iteration – projection maps, bubble plots, and finally a card-based layout – was shaped by insights into user comprehension, trust, and pattern comparison needs. The final interface supports overview-to-detail exploration, pattern organization, and contextual analysis, as demonstrated in a case study on student attrition in Computer Science. We reflect on the lessons learned across iterations and offer guidance for designing interpretable pattern exploration tools for high-dimensional data.</div></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"10 2","pages":"Article 100303"},"PeriodicalIF":3.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0