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About this paper
Di Dai, Bo Liu, Youcheng Li, Haojun Yu, Zhouhang Bian, Quanlin Wu, Dong Wang, Sichen Meng and 4 more
Researchers at Peking University have developed MammoExpert, a mammography dataset integrating multi-phase Chain-of-Thought reasoning annotations and detailed pathological features. This resource enables AI models to achieve improved diagnostic accuracy and interpretability for breast lesion classification, with consistent performance gains observed across diverse mammography datasets and for rare subtypes.
Key insights
- Explicitly modeling radiologists' diagnostic workflow through a multi-stage Chain-of-Thought framework allows AI systems to provide transparent, step-by-step explanations, fostering interpretability and clinical trust.
- Rich, fine-grained annotations of histopathology subtypes and BI-RADS features are essential for training AI models to discern subtle differences, which is critical for accurate diagnosis of rare or morphologically similar lesions.
- Integrating CoT reasoning helps AI models overcome reliance on global visual shortcuts and reduce over-prediction of high-frequency classes, leading to more robust and discriminative analysis in complex cases.
Summary by alphaXiv.
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