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.

翻译：KonfAI是一个专为医学影像任务设计的模块化、可扩展且完全可配置的深度学习框架。用户可通过结构化的YAML配置文件定义完整的训练、推理与评估工作流，而无需修改底层代码。这种声明式方法在提升实验可复现性、透明度与可追溯性的同时，显著减少了开发时间。相较于标准流程，KonfAI原生支持多种高级策略的抽象实现，包括基于图像块的学习、测试时数据增强、模型集成，以及用于深度监督的中间特征表示直接访问。该框架同样支持复杂的多模型训练配置，例如生成对抗网络架构。凭借其模块化与可扩展的架构设计，KonfAI能够轻松集成用户自定义的模型、损失函数与数据处理组件。本框架已成功应用于分割、配准与图像合成等任务，并在多项国际医学影像挑战赛中取得了领先成果。KonfAI为开源项目，可通过 https://github.com/vboussot/KonfAI 获取。