一个连锁思维推理的乳房超声数据集,涵盖了所有组织病理学类别
Haojun Yu1, Youcheng Li1, Zihan Niu2
1State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, 100871, China.
Scientific data
|February 6, 2026
概括
这项研究介绍了BUS-CoT,这是一个大型乳房超声波数据集,为AI开发提供了详细的注释. 它支持人工智能模型中的高级思维链推理,改善罕见病例诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是指放射学
背景情况:
- 乳房超声波 (BUS) 对于诊断乳房病变至关重要,但人工智能开发面临着有限的,低丰度数据集.
- 现有的BUS数据集缺乏高级AI推理所需的规模和详细注释.
研究的目的:
- 引入BUS-CoT,这是一个全面的乳房超声数据集,旨在用于AI中的思维链 (CoT) 推理分析.
- 提供高质量的基准数据集,用于培训和评估AI模型,用于诊断乳腺损伤,包括罕见病例.
主要方法:
- 开发了BUS-CoT,使用了11,439张超声波图像,涵盖了世卫组织99个组织病理学类别中的11,850个病变.
- 策划了一组高质量的5163张病变焦点图像,由专家放射科医生进行注释.
- 由专家验证的构建的COT推理过程 (观察,特征,诊断,病理学).
主要成果:
- BUS-CoT数据集为人工智能开发提供了广泛的数据规模和注释丰富性.
- 包括详细的推理路径,以促进COT分析和提高诊断准确性.
- 涵盖所有类型的组织病理学,帮助AI强度在诊断罕见和具有挑战性的乳腺病变.
结论:
- BUS-CoT解决了当前BUS数据集的局限性,使得诊断推理中的先进AI研究成为可能.
- 该数据集有助于开发更强大,更准确的人工智能系统来对乳腺病变进行分类,特别是在罕见疾病中.
- 数据集和代码的公开可用性促进了人工智能辅助乳房成像的进一步研究和开发.
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