A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories

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.

乳腺超声（Breast Ultrasound, BUS）是乳腺病灶诊断的核心工具，年检查量达数百万次。然而，当前面向人工智能开发的高质量公开乳腺超声基准数据集，在数据规模与标注丰富度两方面均存在不足。本研究提出了BUS-CoT数据集——一款用于思维链（Chain-of-Thought, CoT）推理分析的乳腺超声数据集，该数据集包含来自4838名患者的10019处病灶的11439幅超声图像，涵盖全部99种组织病理学类型。为推动激励思维链推理的相关研究，我们基于观察、特征、诊断及病理标签构建了推理流程，并由经验丰富的专家完成标注与核验。此外，鉴于本数据集覆盖了所有组织病理学类型的病灶，我们旨在助力构建针对罕见病例的鲁棒人工智能系统——此类病例在临床实践中极易出现诊断误差。