MammoClean：通过数据集协调实现乳腺X光摄影中具备偏置感知的可复现AI
MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization
AI总结：
提出MammoClean框架，通过标准化病例选择、图像处理和元数据统一，识别并量化乳腺X光摄影数据集中的偏置与分布偏移，提升AI模型的跨域泛化能力与临床可靠性。
摘要：
乳腺X光摄影中临床可靠的人工智能（AI）系统的开发，受到公共数据集中数据质量、元数据标准和人群分布存在深刻异质性的阻碍。这种异质性引入了数据集特定的偏置，严重损害了模型的泛化能力，这是临床部署的根本障碍。我们提出了MammoClean，一个用于乳腺X光摄影数据集标准化和偏置量化的公开框架。MammoClean标准化了病例选择、图像处理（包括左右侧和强度校正），并将元数据统一为一致的多视图结构。我们提供了对乳腺解剖学、成像特征和公共乳腺X光摄影数据集的全面回顾，以系统地识别偏置的关键来源。将MammoClean应用于三个异构数据集（CBIS-DDSM、TOMPEI-CMMD、VinDr-Mammo），我们量化了乳腺密度和异常患病率方面的显著分布偏移。至关重要的是，我们展示了数据损坏的直接影响：与经过精心整理的对应数据集相比，在损坏数据集上训练的AI模型表现出显著的性能下降。通过使用MammoClean识别和缓解偏置来源，研究人员可以构建统一的多数据集实验语料库，从而开发出具有卓越跨域泛化能力的鲁棒模型。MammoClean为乳腺X光摄影中具备偏置感知的AI开发提供了必不可少且可复现的流程，促进了更公平的比较，并推动了在不同患者人群和临床环境中公平执行的安全、有效产品的创建。开源代码可在以下网址公开获取：https://github.com/Minds-R-Lab/MammoClean。
英文摘要：
The development of clinically reliable artificial intelligence (AI) systems for mammography is hindered by profound heterogeneity in data quality, metadata standards, and population distributions across public datasets. This heterogeneity introduces dataset-specific biases that severely compromise the generalizability of the model, a fundamental barrier to clinical deployment. We present MammoClean, a public framework for standardization and bias quantification in mammography datasets. MammoClean standardizes case selection, image processing (including laterality and intensity correction), and unifies metadata into a consistent multi-view structure. We provide a comprehensive review of breast anatomy, imaging characteristics, and public mammography datasets to systematically identify key sources of bias. Applying MammoClean to three heterogeneous datasets (CBIS-DDSM, TOMPEI-CMMD, VinDr-Mammo), we quantify substantial distributional shifts in breast density and abnormality prevalence. Critically, we demonstrate the direct impact of data corruption: AI models trained on corrupted datasets exhibit significant performance degradation compared to their curated counterparts. By using MammoClean to identify and mitigate bias sources, researchers can construct unified multi-dataset training corpora that enable development of robust models with superior cross-domain generalization. MammoClean provides an essential, reproducible pipeline for bias-aware AI development in mammography, facilitating fairer comparisons and advancing the creation of safe, effective systems that perform equitably across diverse patient populations and clinical settings. The open-source code is publicly available from: https://github.com/Minds-R-Lab/MammoClean.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
