Abstract: DIY Challenge Blueprint: from organization to technical implementation in Biomedical Image Analysis

  • The high cost of challenge platforms prevents many people from organizing their own competitions. The do-it-yourself (DIY) challenge blueprint [1] allows you to host your own biomedical AI benchmark challenge. Our DIY approach circumvents the current constraints of commercial challenge platforms. A sovereign, extensible and cost-efficient deployment is provided via containerised, identity-managedThe high cost of challenge platforms prevents many people from organizing their own competitions. The do-it-yourself (DIY) challenge blueprint [1] allows you to host your own biomedical AI benchmark challenge. Our DIY approach circumvents the current constraints of commercial challenge platforms. A sovereign, extensible and cost-efficient deployment is provided via containerised, identity-managed and reproducible pipelines. Focus lies on GDPR-compliant hosting via infrastructure-as-code, automated evaluation, modular orchestration, and role-based identity and access management. The framework integrates Docker-based execution and standardised interfaces for task definitions, dataset curation and evaluation. All in all it is designed to be flexible and modular, as demonstrated in the MICCAI 2024 PhaKIR challenge [2, 3]. In this case study, different medical tasks on a multicentre laparoscopic dataset with framewise labels for phases and spatial annotations for instruments across fulllength videos were supported. This case study empirically validates the DIY challenge blueprint as a reproducible and customizable challenge-hosting infrastructure. The full code can be found at https://github.com/remic-othr/PhaKIR_DIY.show moreshow less

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Metadaten
Author:Leonard Klausmann, Tobias Rueckert, David RauberOTH, Raphaela Maerkl, Suemeyye R. Yildiran, Max GutbrodORCiD, Christoph PalmOTHORCiDGND
DOI:https://doi.org/10.1007/978-3-658-51100-5_27
Parent Title (English):Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Lübeck March 15-17, 2026
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Editor:Heinz Handels, Katharina Breininger, Thomas M. Deserno, Andreas MaierOTH, Klaus H. Maier-HeinORCiD, Christoph PalmOTH, Thomas TolxdorffORCiDGND
Document Type:conference proceeding (presentation, abstract)
Language:English
Year of first Publication:2026
Release Date:2026/03/30
GND Keyword:Bildverarbeitung
First Page:131
Last Page:131
Andere Schriftenreihe:Informatik aktuell
Andere Schriftenreihe:BVM Workshop
Institutes:Fakultät Informatik und Mathematik
Research Center of Biomedical Engineering - RCBE
Research Center of Health Sciences and Technology - RCHST
Research Center for Artificial Intelligence - RCAI
Fakultät Informatik und Mathematik / Labor Regensburg Medical Image Computing (ReMIC)
DFG subject classification:Ingenieurwissenschaften
research focus:Gesundheit und Soziales
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG
Frontdoor-URL:https://opus4.kobv.de/opus4-oth-regensburg/8977
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