2025
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@inproceedings{alhazwani2025datahum,
author = {Al-Hazwani, Ibrahim and Zhang, Ke Er Amy and Garrison, Laura and Bernard, J{\"u}rgen},
title = {Data Humanism decoded: A characterization of its principles to bridge
data visualization researchers and practitioners},
booktitle = {Proceedings of IEEE VIS 2025 (Short Papers)--in press"},
year = {2025},
numpages = {5},
publisher = {IEEE Computer Society},
address = {Los Alamitos},
abstract = {Data Humanism is a human-centered design approach that emphasizes the personal, contextual, and imperfect nature of data. Despite its growing influence among practitioners, the 13 principles outlined in Giorgia Lupi’s visual manifesto remain loosely defined in research contexts, creating a gap between design practice and systematic application. Through a mixed-methods approach, including a systematic literature review, multimedia analysis, and expert interviews, we present a characterization of Data Humanism principles for visualization researchers. Our characterization provides concrete definitions that maintain interpretive flexibility in operationalizing design choices. We validate our work through direct consultation with Lupi. Moreover, we leverage the characterization to decode a visualization work, mapping Data Humanism principles to specific visual design choices. Our work creates a common language for human-centered visualization, bridging the gap between practice and research for future applications and evaluations.},
pdf = {pdfs/alhazwani2025datahum.pdf},
images = {images/alhazwani2025datahum.png},
thumbnails = {images/alhazwani2025datahum_thumb.png},
}
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@article{mittenentzwei2025icom,
title = {AI-based character generation for disease stories: A case study using epidemiological data to highlight preventable risk factors},
author = {Mittenentzwei, Sarah and Garrison, Laura A. and Budich, Beatrice and Lawonn, Kai and Dockhorn, Alexander and Preim, Bernhard and Meuschke, Monique},
year = 2025,
journal = {i-com},
publisher = {De Gruyter},
pages = {},
doi = {10.1515/icom-2024-0041},
abstract = {Data-driven storytelling has grown significantly, becoming prevalent in various fields, including healthcare. In medical narratives, characters are crucial for engaging audiences, making complex medical information accessible, and potentially influencing positive behavioral and lifestyle changes. However, designing characters that are both educational and relatable to effectively engage audiences is challenging. We propose a GenAI-assisted pipeline for character design in data-driven medical stories, utilizing Stable Diffusion, a deep learning text-to-image model, to transform data into visual character representations. This approach reduces the time and artistic skills required to create characters that reflect the underlying data. As a proof-of-concept, we generated and evaluated two characters in a crowd-sourced case study, assessing their authenticity to the underlying data and consistency over time. In a qualitative evaluation with four experts with knowledge in design and health communication, the characters were discussed regarding their quality and refinement opportunities. The characters effectively conveyed various aspects of the data, such as emotions, age, and body weight. However, generating multiple consistent images of the same character proved to be a significant challenge. This underscores a key issue in using generative AI for character creation: the limited control designers have over the output.},
images = {images/mittenentzwei2025icom.png},
thumbnails = {images/mittenentzwei2025icom_thumb.png},
pdf = {pdfs/mittenentzwei2025icom.pdf}
}
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@inproceedings{zhang2025stories,
author = {Zhang, Ke Er Amy and Garrison, Laura},
title = {Modern snapshots in the crafting of a medical illustration},
booktitle = {Proceedings of CHI '25 Workshop "How do design stories work? Exploring narrative forms of knowledge in HCI"},
year = {2025},
numpages = {3},
abstract = {The time-honored practice of medical illustration and visualization, has, like nearly all other disciplines, seen changes in its tooling and development pipeline in step with technological and societal developments. At its core, however, medical visualization remains a discipline focused on telling stories about biology and medicine. The story we tell in this work assumes a more distant vantage point to tell a story about the biomedical storytellers themselves. Our story peers over the shoulders of two medical illustrators in the middle of a project to illustrate a procedure in one of the small blood vessels around the heart, and through the medium of an online chat explores the dialogue, tensions, and goals of such projects in the digital age. We adopt the two-column format of the CHI template, as it is more reminiscent of the width of our usual messaging windows while working. The second part of our submission reflects on these tensions and modes of storytelling from an HCI and Visualization-situated perspective.},
pdf = {pdfs/zhang2025stories.pdf},
images = {images/zhang2025stories.png},
thumbnails = {images/zhang2025stories.png},
project = {VIDI}
}
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@article{vandenbossche2025open,
title = {The Open Anatomy Explorer--a journey towards accessible open-source 3D learning environments},
author = {Vandenbossche, Vicky and Van Kenhove, Michiel and Smit, Noeska and Willaert, Wouter and De Turck, Filip and Volckaert, Bruno and Valcke, Martin and Audenaert, Emmanuel},
year = 2025,
journal = {Journal of Visual Communication in Medicine},
publisher = {Taylor \& Francis},
pages = {1--12},
doi = {10.1080/17453054.2024.2446764},
url = {https://www.tandfonline.com/doi/full/10.1080/17453054.2024.2446764},
abstract = {Anatomy learning has traditionally relied on drawings, plastic models, and cadaver dissections/prosections to help students understand the three-dimensional (3D) relationships within the human body. However, the landscape of anatomy education has been transformed with the introduction of digital media. In this light, the Open Anatomy Explorer (OPANEX) was developed. It includes two user interfaces (UI): one for students and one for administrators. The administrator UI offers features such as uploading and labelling of 3D models, and customizing 3D settings. Additionally, the OPANEX facilitates content sharing between institutes through its import-export functionality. To evaluate the integration of OPANEX within the existing array of learning resources, a survey was conducted as part of the osteology course at Ghent University, Belgium. The survey aimed to investigate the frequency of use of five learning resources, attitudes towards 3D environments, and the OPANEX user experience. Analysis revealed that the OPANEX was the most frequently used resource. Students’ attitudes towards 3D learning environments further supported this preference. Feedback on the OPANEX user experience indicated various reasons for its popularity, including the quality of the models, regional annotations, and customized learning content. In conclusion, the outcomes underscore the educational value of the OPANEX, reflecting students’ positive attitudes towards 3D environments in anatomy education.},
images = {images/vandenbossche2025open.png},
thumbnails = {images/vandenbossche2025open.png},
pdf = {pdfs/vandenbossche2025open.pdf}
}
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@article{wagner2025mri,
title = {MRI delta radiomics during chemoradiotherapy for prognostication in locally advanced cervical cancer},
author = {Wagner-Larsen, Kari S and Lura, Njål and Gulati, Ankush and Ryste, Stian and Hodneland, Erlend and Fasmer, Kristine E and Woie, Kathrine and Bertelsen, Bjørn I and Salvesen, Øyvind and Halle, Mari K and Smit, Noeska and Krakstad, Camilla and Haldorsen, Ingfrid S},
year = 2025,
journal = {BMC cancer},
volume = 25,
pages = 122,
doi = {10.1186/s12885-025-13509-1},
url = {https://bmccancer.biomedcentral.com/articles/10.1186/s12885-025-13509-1},
abstract = {Effective diagnostic tools for prompt identification of high-risk locally advanced cervical cancer (LACC) patients are needed to facilitate early, individualized treatment. The aim of this work was to assess temporal changes in tumor radiomics (delta radiomics) from T2-weighted imaging (T2WI) during concurrent chemoradiotherapy (CCRT) in LACC patients, and their association with progression-free survival (PFS). Furthermore, to develop, validate, and compare delta- and pretreatment radiomic signatures for prognostic modeling.},
images = {images/wagner2025mri.png},
thumbnails = {images/wagner2025mri.png},
pdf = {pdfs/wagner2025mri.pdf}
}
2024
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@article{splechtna2024interactive,
title={Interactive design-of-experiments: optimizing a cooling system},
author={Splechtna, Rainer and Behravan, Majid and Jelovic, Mario and Gracanin, Denis and Hauser, Helwig and Matkovic, Kre{\v{s}}imir},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
publisher={IEEE},
doi = {10.1109/TVCG.2024.3456356},
url = {https://doi.org/10.1109/TVCG.2024.3456356},
images = {images/splechtna2024cooling.png},
thumbnails = {images/splechtna2024cooling.png},
pdf = {pdfs/splechtna2024cooling.pdf},
abstract = {The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space.The extent of the parameter space, the complexity of the non-linear model of the system,as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other.The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation.When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.}
}