KonfAI让医学影像深度学习实验配置更简单，无需改代码即可完成全流程训练与评估。
KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging
- 通过YAML文件定义全流程，无需修改代码即可配置训练推理流程。
- 支持切片学习、测试时增强、模型集成等高级策略，提升模型性能。
- 适合需要快速搭建可复现医学影像实验的科研人员与开发者使用。
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、推理和评估流程，无需修改底层代码。这种声明式方法提升了实验的可复现性、透明度和可追溯性，同时减少开发时间。除了标准流水线功能外，KonfAI 提供了对切片学习、测试时增强、模型集成以及中间特征表示直接访问等高级策略的原生抽象，支持生成对抗架构等复杂多模型训练。其模块化设计可轻松集成自定义模型、损失函数和数据处理组件。该框架已成功应用于分割、配准和图像合成任务，并在多个国际医学影像挑战赛中取得领先结果。KonfAI 已开源，项目地址：https://github.com/vboussot/KonfAI。
原文摘要 · Abstract (English)
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.
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