DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study

In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while sav...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2025-01, Vol.31 (1), p.54-64
Main Authors: Wyss, Alexander, Morgenshtern, Gabriela, Hirsch-Husler, Amanda, Bernard, Jurgen
Format: Article
Language:English
Subjects:
Anomaly Detection
Contamination
Data augmentation
Data Labeling
Decision making
Diseases
Image Data
Knowledge Externalization
Labeling
Medical Manufacturing
Production
Quality assessment
Visual Analytics
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Summary:In medical diagnostics of both early disease detection and routine patient care, particle-based contamination of in-vitro diagnostics consumables poses a significant threat to patients. Objective data-driven decision-making on the severity of contamination is key for reducing patient risk, while saving time and cost in quality assessment. Our collaborators introduced us to their quality control process, including particle data acquisition through image recognition, feature extraction, and attributes reflecting the production context of particles. Shortcomings in the current process are limitations in exploring thousands of images, data-driven decision making, and ineffective knowledge externalization. Following the design study methodology, our contributions are a characterization of the problem space and requirements, the development and validation of DaedalusData, a comprehensive discussion of our study's learnings, and a generalizable framework for knowledge externalization. DaedalusData is a visual analytics system that enables domain experts to explore particle contamination patterns, label particles in label alphabets, and externalize knowledge through semi-supervised label-informed data projections. The results of our case study and user study show high usability of DaedalusData and its efficient support of experts in generating comprehensive overviews of thousands of particles, labeling of large quantities of particles, and externalizing knowledge to augment the dataset further. Reflecting on our approach, we discuss insights on dataset augmentation via human knowledge externalization, and on the scalability and trade-offs that come with the adoption of this approach in practice.
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2024.3456329