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

arXiv: https://arxiv.org/abs/2509.17046 · HTML: https://mlsanity.com/paper/2509.17046/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 197 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [Out-of-distribution Detection in Medical Image Analysis: A survey](https://mlsanity.com/paper/2404.18279/) (2024)
- [Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses](https://mlsanity.com/paper/2407.16634/) (2024)
- [A Foundational Generative Model for Breast Ultrasound Image Analysis](https://mlsanity.com/paper/2501.06869/) (2025)

## Built on by (most cited first, 6 of 6)

- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine](https://mlsanity.com/paper/2606.04911/) (2026)
- [Experience-Guided Self-Adaptive Cascaded Agents for Breast Cancer Screening and Diagnosis with Reduced Biopsy Referrals](https://mlsanity.com/paper/2602.23899/) (2026)
- [UltrasoundAgents: Hierarchical Multi-Agent Evidence-Chain Reasoning for Breast Ultrasound Diagnosis](https://mlsanity.com/paper/2603.10852/) (2026)
- [Ultrasound Vision-Language Alignment via Contrastive Learning](https://mlsanity.com/paper/2605.02126/) (2026)
- [Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming](https://mlsanity.com/paper/2605.21652/) (2026)

## Abstract (arXiv)

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.