Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
Abstract
Unstructured data in industries such as healthcare, finance, and manufacturing presents significant challenges for efficient analysis and decision making. Detecting patterns within this data and understanding their impact is critical but complex without the right tools. Traditionally, these tasks relied on the expertise of data analysts or labor-intensive manual reviews. In response, we introduce Spacewalker, an interactive tool designed to explore and annotate data across multiple modalities. Spacewalker allows users to extract data representations and visualize them in low-dimensional spaces, enabling the detection of semantic similarities. Through extensive user studies, we assess Spacewalker’s effectiveness in data annotation and integrity verification. Results show that the tool’s ability to traverse latent spaces and perform multi-modal queries significantly enhances the user’s capacity to quickly identify relevant data. Moreover, Spacewalker allows for annotation speed-ups far superior to conventional methods, making it a promising tool for efficiently navigating unstructured data and improving decision making processes. The code of this work is open-source and can be found at: https://github.com/code-lukas/Spacewalker
Lukas Heine
Team Lead Ophthalmology
Fabian Hörst
Team Lead Computer Vision and Computational Pathology
Jana Fragemann
PhD Student
Gijs Luijten
PhD Student
Jan Egger
Team Lead AI-guided Therapies
Jens Kleesiek
Professor of Translational Image-guided Oncology
