A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915508192804864 |
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| author | Yu, Haojun Li, Youcheng Niu, Zihan Zhang, Nan Gong, Xuantong Li, Huan Zou, Zhiying Qi, Haifeng Cao, Zhenxiao Lan, Zijie Yuan, Xingjian He, Jiating Zhang, Haokai Zhang, Shengtao Wang, Zicheng Wang, Dong Zhao, Ziwei Chen, Congying Wang, Yong Qin, Wangyan Zhu, Qingli Wang, Liwei |
| author_facet | Yu, Haojun Li, Youcheng Niu, Zihan Zhang, Nan Gong, Xuantong Li, Huan Zou, Zhiying Qi, Haifeng Cao, Zhenxiao Lan, Zijie Yuan, Xingjian He, Jiating Zhang, Haokai Zhang, Shengtao Wang, Zicheng Wang, Dong Zhao, Ziwei Chen, Congying Wang, Yong Qin, Wangyan Zhu, Qingli Wang, Liwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories Yu, Haojun Li, Youcheng Niu, Zihan Zhang, Nan Gong, Xuantong Li, Huan Zou, Zhiying Qi, Haifeng Cao, Zhenxiao Lan, Zijie Yuan, Xingjian He, Jiating Zhang, Haokai Zhang, Shengtao Wang, Zicheng Wang, Dong Zhao, Ziwei Chen, Congying Wang, Yong Qin, Wangyan Zhu, Qingli Wang, Liwei Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.17046 |