KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging
This framework addresses reproducibility and efficiency issues for researchers and practitioners in medical imaging, though it is incremental as it builds on existing pipeline concepts.
The authors tackled the challenge of developing deep learning workflows for medical imaging by creating KonfAI, a modular and configurable framework that uses YAML files to define training, inference, and evaluation, resulting in top-ranking outcomes in international challenges.
KonfAI is a modular, extensible, and fully configurable deep learning framework specifically designed for medical imaging tasks. It enables users to define complete training, inference, and evaluation workflows through structured YAML configuration files, without modifying the underlying code. This declarative approach enhances reproducibility, transparency, and experimental traceability while reducing development time. Beyond the capabilities of standard pipelines, KonfAI provides native abstractions for advanced strategies including patch-based learning, test-time augmentation, model ensembling, and direct access to intermediate feature representations for deep supervision. It also supports complex multi-model training setups such as generative adversarial architectures. Thanks to its modular and extensible architecture, KonfAI can easily accommodate custom models, loss functions, and data processing components. The framework has been successfully applied to segmentation, registration, and image synthesis tasks, and has contributed to top-ranking results in several international medical imaging challenges. KonfAI is open source and available at https://github.com/vboussot/KonfAI.