学生黑客松复现三大经典MRI重建方法，验证可复现性。
A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods
- 组织学生复现3篇重要MRI重建论文的代码与结果。
- 成功复现MoDL、HUMUS-Net及物理约束动态成像方法。
- 强调代码可复现性，提供实践指南适合科研新人参考。
我们报告了一次聚焦于复现三篇具有影响力的MRI重建论文的学生可复现性黑客松的设计、流程与成果：(a) MoDL，一种带学习去噪的展开式模型基网络；(b) HUMUS-Net，一种混合展开式多尺度CNN+Transformer架构；(c) 一种无需训练、基于物理模型的动态MRI方法，利用定量MR模型实现早停。本文描述了黑客松的组织方式，展示了复现结果及额外实验，并详细阐述了构建可复现代码库的基本实践。
原文摘要 · Abstract (English)
We report the design, protocol, and outcomes of a student reproducibility hackathon focused on replicating the results of three influential MRI reconstruction papers: (a) MoDL, an unrolled model-based network with learned denoising; (b) HUMUS-Net, a hybrid unrolled multiscale CNN+Transformer architecture; and (c) an untrained, physics-regularized dynamic MRI method that uses a quantitative MR model for early stopping. We describe the setup of the hackathon and present reproduction outcomes alongside additional experiments, and we detail fundamental practices for building reproducible codebases.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。