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）是诊断乳腺病变的核心工具，每年全球开展的相关检查数量高达数百万次。然而，当前面向人工智能开发的高质量乳腺超声基准数据集，在数据规模与标注丰富度层面均存在明显不足。本研究提出BUS-CoT数据集——一款面向思维链（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处病变的11439幅超声图像，涵盖全部99种组织病理学类型。为推动思维链推理相关研究的发展，我们基于观测、特征、诊断与病理标签构建了标准化推理流程，并由资深临床与病理专家完成标注与核验工作。此外，由于该数据集覆盖了所有组织病理学类型的病变，我们旨在助力开发鲁棒性更强的人工智能系统，以应对临床实践中极易出现诊断误差的罕见病例场景。

搜集汇总

背景与挑战

背景概述

BUS-CoT是一个全面的乳腺超声数据集，包含11,439张图像，覆盖所有99种组织病理学类型，专为链式思维推理分析设计。数据集提供详细的专家标注推理过程，旨在支持AI在乳腺病变诊断中的研究，特别是在罕见病例中的应用。