一个模块化医学图像分割框架，支持多种前沿训练范式。
APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms
- 用 YAML 配置驱动模块化设计，组件可复用
- 集成半监督、领域自适应等 5 种先进范式
- 适合研究者快速实验与代码复现
我们提出 APRIL-MedSeg，一个基于 YAML 驱动的 2D 医学图像分割模块化框架。该框架将分割网络分解为可复用组件，提供统一且可扩展的生态系统。它整合了半监督学习、领域自适应、知识蒸馏、弱监督学习、文本引导分割及基础模型支持等多种前沿范式。基于注册表的配置系统支持继承机制，实现灵活且可复现的实验管理，可无缝切换模型、数据集与训练策略。框架还提供统一接口，涵盖医学数据集、增强管道、部署工具与模型融合。整体设计旨在连接算法创新与实际部署，构建系统化的医学图像分割研究生态。代码开源于 https://github.com/juntaoJianggavin/APRIL-MedSeg，采用 Apache 2.0 许可证。
原文摘要 · Abstract (English)
We present APRIL-MedSeg, a YAML-driven modular framework for 2D medical image segmentation. It provides a unified and extensible ecosystem that decomposes segmentation networks into reusable components. Also, the framework integrates a broad spectrum of advanced paradigms, including semi-supervised learning, domain adaptation, knowledge distillation, weakly supervised learning, and text-guided segmentation as well as foundation model support. A registry-based configuration system with inheritance enables flexible and reproducible experiment management, supporting seamless switching across models, datasets, and training strategies. In addition, the framework provides a unified interface for medical datasets, augmentation pipelines, deployment utilities and model ensembling. Overall, APRIL-MedSeg is designed as a general-purpose research and development platform that bridges algorithmic innovation and practical deployment, while also serving as a structured ecosystem for systematically organizing and reproducing advances in medical image segmentation. The code is available at https://github.com/juntaoJianggavin/APRIL-MedSeg under an Apache 2.0 license.
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