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<p>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 ultrasound images from 11,850 lesions and 4,838 patients, covering all 99 WHO-defined histopathology categories. For model training and evaluation, we provide a curated high-quality subset of 5,163 lesion-focused images annotated by experienced radiologists. 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. The data and code are publicly available at <a href="https://doi.org/10.6084/m9.figshare.30838715">https://doi.org/10.6084/m9.figshare.30838715</a>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Haojun Yu,
  • Youcheng Li,
  • Zihan Niu,
  • Nan Zhang,
  • Xuantong Gong,
  • Huan Li,
  • Zhiying Zou,
  • Haifeng Qi,
  • Zhenxiao Cao,
  • Zijie Lan,
  • Xingjian Yuan,
  • Jiating He,
  • Haokai Zhang,
  • Shengtao Zhang,
  • Zicheng Wang,
  • Dong Wang,
  • Ziwei Zhao,
  • Congying Chen,
  • Yong Wang,
  • Wangyan Qin,
  • Qingli Zhu,
  • Liwei Wang

摘要

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 ultrasound images from 11,850 lesions and 4,838 patients, covering all 99 WHO-defined histopathology categories. For model training and evaluation, we provide a curated high-quality subset of 5,163 lesion-focused images annotated by experienced radiologists. 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. The data and code are publicly available at https://doi.org/10.6084/m9.figshare.30838715.