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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | DaedalusData: Exploring and Labeling of a Large High-Dimensional Unlabeled Image Dataset; Analysis of Particle Contamination in Global Operations Consumables |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | With the surge in the volume and dimensionality of large image datasets across fields such as medicine, manufacturing, and quality monitoring, there is an increased emphasis on efficiently curating these datasets. This design study explores the challenges associated with labeling and exploring large, high-dimensional, and unlabeled image datasets. Traditional tools prioritize either data visualization using techniques like dimensionality reduction or labeling automation using AI learning mechanisms. This binary focus often comes at the cost of extensive labeling functionalities or comprehensive overviews, since user interaction is reduced. This research bridges this gap by introducing DaedalusData, an interactive visual analytics approach that combines meaningful visual exploration with efficient labeling and intuitive feedback loops. DaedalusData presents an interactive platform that enables pattern and anomaly exploration, efficient image labeling by integrating metadata, and preliminary steps toward labeling automation. The tool was developed alongside domain experts and built for a dataset containing particle contamination in consumables at Roche Diagnostics. As a design study this thesis, solved a real-world problem, through close collaboration with domain experts. The study posits that merging interactivity, human expertise, and automated processes offer a promising direction for managing large image datasets, with DaedalusData serving as a foundational step. |
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