首个公开的多期腹部CT肝癌评估数据集，助力AI精准诊断
AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
- 整合四大数据集并由专家标注，覆盖590例多期CT
- 包含病灶大小、位置及三类关键影像特征的精确标注
- 适合医学影像AI研究者用于肝癌智能诊断模型训练与评测
肝细胞癌（HCC）是全球癌症致死率第三高的疾病，早期发现可使生存率从不足20%提升至70%以上。标准化的肝脏影像报告与数据系统（LI-RADS）为肝占位性病变的HCC评估提供了影像学诊断框架，是推动人工智能（AI）自动识别HCC的基础。然而，缺乏大规模、高质量标注的公开数据集严重制约了相关AI模型的发展与评估。本文提出AMPLIFAI数据集，是首个公开的多期腹部CT数据集，包含590例患者的研究，由五名注册放射科医生和一名住院医师进行专家标注，涵盖LI-RADS分类、病灶大小以及动脉期强化、洗脱和强化包膜三个主要特征的体素级分割。数据集源自四个公开数据库，经过统一处理与标准化，并依据《数据集说明书》格式详细描述其构成、清洗与标注流程，以支持医学影像AI领域的透明化、可复现研究。
原文摘要 · Abstract (English)
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from <20% to >70%. The standardized Liver Imaging Reporting and Data System (LI-RADS) criteria provide an imaging-based diagnostic framework to evaluate liver lesions for HCC, serving as a foundation for automating HCC detection with artificial intelligence (AI). However, the lack of large, publicly available datasets with high-quality annotations has limited the development and evaluation of AI models for automated LI-RADS assessment. We introduce AMPLIFAI dataset, the first public dataset of 590 multiphase abdominal CT studies annotated with LI-RADS categories, lesion size, and voxel-level segmentations for three major LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. The dataset was curated and harmonized from four public datasets and augmented with expert annotations from five board-certified radiologists and one resident. Following the Datasheets for Datasets format, this paper details the dataset's composition, curation and harmonization process, and annotation workflow to support transparent, reproducible research in medical imaging AI.
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。