构建首个覆盖全部乳腺病理类型的超声推理数据集，助力AI精准诊断。
A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
- 基于观察、特征、诊断与病理标签构建链式思维推理流程
- 含1.1万张图像、1万多个病灶，覆盖99种病理类型
- 专为罕见病诊断设计，适合开发鲁棒性AI系统
乳腺超声（BUS）是每年数百万例乳腺病变筛查的重要工具。然而，当前公开的高质量BUS数据集在规模和标注丰富度上仍显不足。本文提出BUS-CoT数据集，用于链式思维（CoT）推理分析，包含来自4,838名患者的10,019个病灶、共计11,439张图像，覆盖全部99种组织病理学类型。通过经验丰富的专家标注与验证，构建了基于观察、特征、诊断及病理标签的推理过程。该数据集旨在推动链式思维推理研究，并提升人工智能系统在罕见病例中的表现，降低临床误诊风险。
原文摘要 · Abstract (English)
Breast ultrasound (BUS) is an essential tool for diagnosing breast lesions, with millions of examinations per year. However, publicly available high-quality BUS benchmarks for AI development are limited in data scale and annotation richness. In this work, we present BUS-CoT, a BUS dataset for chain-of-thought (CoT) reasoning analysis, which contains 11,439 images of 10,019 lesions from 4,838 patients and covers all 99 histopathology types. To facilitate research on incentivizing CoT reasoning, we construct the reasoning processes based on observation, feature, diagnosis and pathology labels, annotated and verified by experienced experts. Moreover, by covering lesions of all histopathology types, we aim to facilitate robust AI systems in rare cases, which can be error-prone in clinical practice.
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