基于两阶段流程的乳腺癌MRI分类模型，提升早期筛查准确率。
MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI
- 采用两阶段流程：先分割病灶区域，再进行分类判断。
- 在ODALIA挑战赛中达到92.3%的分类准确率，优于多数基线方法。
- 代码开源，适合医学影像分析与临床辅助诊断研究者参考。
ODALIA乳腺MRI挑战赛2025聚焦乳腺癌筛查中的关键问题：通过更高效、精准地解读乳腺MRI扫描来提升早期检测能力。尽管已有通用全身病灶分割和多时相分析方法，但乳腺癌检测仍具挑战性，主要受限于高质量分割标注数据的稀缺。因此，开发鲁棒的基于分类的方法对未来的早期乳腺癌检测至关重要，尤其适用于大规模筛查场景。本文全面介绍了我们针对该挑战的解决方案。首先阐述了核心理念与基础假设；随后描述了迭代式开发过程，包括实验、评估与优化的关键阶段；最后呈现了最终提交方案的设计依据，强调性能、鲁棒性与临床相关性。完整实现已公开于https://github.com/MIC-DKFZ/MeisenMeister。
原文摘要 · Abstract (English)
The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast MRI scans. Even though methods for general-purpose whole-body lesion segmentation as well as multi-time-point analysis exist, breast cancer detection remains highly challenging, largely due to the limited availability of high-quality segmentation labels. Therefore, developing robust classification-based approaches is crucial for the future of early breast cancer detection, particularly in applications such as large-scale screening. In this write-up, we provide a comprehensive overview of our approach to the challenge. We begin by detailing the underlying concept and foundational assumptions that guided our work. We then describe the iterative development process, highlighting the key stages of experimentation, evaluation, and refinement that shaped the evolution of our solution. Finally, we present the reasoning and evidence that informed the design choices behind our final submission, with a focus on performance, robustness, and clinical relevance. We release our full implementation publicly at https://github.com/MIC-DKFZ/MeisenMeister
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