IEEE VIS 2024 Content: DaedalusData: Exploration, Knowledge Externalization and Labeling of Particles in Medical Manufacturing - A Design Study

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

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 Alexander Wyss - Roche pRED, Basel, Switzerland. University of Zürich, Zürich, Switzerland

 Gabriela Morgenshtern - University of Zurich, Zurich, Switzerland. Digital Society Initiativ, Zurich, Switzerland

 Amanda Hirsch-Hüsler - Roche Diagnostics International, Rotkreuz, Switzerland

 Jürgen Bernard - University of Zurich, Zurich, Switzerland. Digital Society Initiativ, Zurich, Switzerland

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Exemplar figure, described by caption below — The DaedalusData framework supports two control modes for experts to steer the particle display with, shown here as a 2 × 2 matrix. Vertical: Experts choose between the Attribute View (for one attribute) and the Projection View (for multiple user-specified attributes) to identify areas of interest, and discover similar particles to label. Horizontal: Experts choose to explore either the Pre-Existing Data Attributes (the Image & Production Context), or to extend the exploration to Augmented Data Attributes created through particle labeling (Expert Knowledge). This design study implements a systematic cross-cut of all four types of control, addressing expert-contributed design requirements.

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Keywords

Visual Analytics, Image Data, Knowledge Externalization, Data Labeling, Anomaly Detection, Medical Manufacturing

Abstract

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.