AMPLIFAI
AMPLIFAI是用于肝癌LI-RADS评估的多相腹部CT数据集，包含590例扫描及专家标注的病灶分割掩膜，推动AI早期检测。
基本信息
资源简介
AMPLIFAI
【数据集背景】
本数据集源站标题为《arXiv Query: search_query=&id_list=2608.14778v2&start=0&max_results=1》。源站首次发布：2026-08-14。
【数据内容】
源站原始描述：Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from 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 develo…
【主题与分类】
主题标签：cs.CV、cs.LG。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://arxiv.org/abs/2608.14778v2
下载信息
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使用方式
数据集说明
AMPLIFAI 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2608.14778v2
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract: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.
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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