Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments

TL;DR

Kaapana is an open-source platform that unifies data management, processing, and collaboration tools for medical imaging research, facilitating large-scale, multi-center AI development while maintaining data privacy and reproducibility.

Contribution

It introduces a modular, extensible framework that integrates workflows and user interfaces, enabling scalable, reproducible, and collaborative medical imaging research across institutions.

Findings

Supports diverse research use cases from local to national levels.

Enhances reproducibility and collaboration in multi-center studies.

Allows institutions to retain control over sensitive data.

Abstract

Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, leveraging real-world imaging data in clinical research environments is still hampered by strict regulatory constraints, fragmented software infrastructure, and the challenges inherent in conducting large-cohort multicentre studies. This leads to projects that rely on ad-hoc toolchains that are hard to reproduce, difficult to scale beyond single institutions and poorly suited for collaboration between clinicians and data scientists. We present Kaapana, a comprehensive open-source platform for medical imaging research that is designed to bridge this gap. Rather than building single-use, site-specific tooling, Kaapana provides a modular, extensible framework that unifies data ingestion, cohort curation, processing workflows and result inspection under a common user interface. By bringing the algorithm to the data, it enables institutions to keep control over their sensitive data while still participating in distributed experimentation and model development. By integrating flexible workflow orchestration with user-facing applications for researchers, Kaapana reduces technical overhead, improves reproducibility and enables conducting large-scale, collaborative, multi-centre imaging studies. We describe the core concepts of the platform and illustrate how they can support diverse use cases, from local prototyping to nation-wide research networks. The open-source codebase is available at https://github.com/kaapana/kaapana