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

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

arXiv · 2509.17046

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

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Abstract

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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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. 2025-09-21. A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories. https://arxiv.org/abs/2509.17046

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