DIY challenge blueprint: from organization to technical realization in biomedical image analysis
- Biomedical image analysis challenges have become the de facto standard for publishing new datasets and benchmarking different state-of-the-art algorithms. Most challenges use commercial cloud-based platforms, which can limit custom options and involve disadvantages such as reduced data control and increased costs for extended functionalities. In contrast, Do-It-Yourself (DIY) approaches have theBiomedical image analysis challenges have become the de facto standard for publishing new datasets and benchmarking different state-of-the-art algorithms. Most challenges use commercial cloud-based platforms, which can limit custom options and involve disadvantages such as reduced data control and increased costs for extended functionalities. In contrast, Do-It-Yourself (DIY) approaches have the capability to emphasize reliability, compliance, and custom features, providing a solid basis for low-cost, custom designs in self-hosted systems. Our approach emphasizes cost efficiency, improved data sovereignty, and strong compliance with regulatory frameworks, such as the GDPR. This paper presents a blueprint for DIY biomedical imaging challenges, designed to provide institutions with greater autonomy over their challenge infrastructure. Our approach comprehensively addresses both organizational and technical dimensions, including key user roles, data management strategies, and secure, efficient workflows. Key technical contributions include a modular, containerized infrastructure based on Docker, integration of open-source identity management, and automated solution evaluation workflows. Practical deployment guidelines are provided to facilitate implementation and operational stability. The feasibility and adaptability of the proposed framework are demonstrated through the MICCAI 2024 PhaKIR challenge with multiple international teams submitting and validating their solutions through our self-hosted platform. This work can be used as a baseline for future self-hosted DIY implementations and our results encourage further studies in the area of biomedical image analysis challenges.…


| Author: | Leonard KlausmannORCiD, Tobias RueckertORCiD, David RauberORCiD, Raphaela MaerklORCiD, Suemeyye R. Yildiran, Max GutbrodORCiD, Christoph PalmORCiD |
|---|---|
| DOI: | https://doi.org/10.1007/978-3-032-05141-7_9 |
| ISBN: | 978-3-032-05141-7 |
| Parent Title (English): | Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 ; Proceedings Part XI |
| Publisher: | Springer |
| Place of publication: | Cham |
| Document Type: | conference proceeding (article) |
| Language: | English |
| Year of first Publication: | 2025 |
| Release Date: | 2025/11/11 |
| Tag: | Biomedical challenges; Blueprint; Do-It-Yourself; Image analysis; Self-hosting |
| First Page: | 85 |
| Last Page: | 95 |
| Konferenzangabe: | 28th International Conference, 23-27 September 2025, Daejeon, South Korea |
| Andere Schriftenreihe: | Lecture Notes in Computer Science, volume 15970 |
| Institutes: | Fakultät Informatik und Mathematik |
| Regensburg Center of Health Sciences and Technology - RCHST | |
| Fakultät Informatik und Mathematik / Labor Regensburg Medical Image Computing (ReMIC) | |
| Begutachtungsstatus: | peer-reviewed |
| research focus: | Digitale Transformation |
| Licence (German): |


