MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis
For researchers in medical AI, this provides a benchmark dataset and method for interpretable mammography diagnosis, though the gains are incremental over existing models.
The authors introduce MammoExpert, the first mammography dataset with Chain-of-Thought reasoning annotations, and show that combining it with public data improves classification accuracy by 7.1%, with an additional 4% gain from CoT training, achieving 6.9% and 6.7% gains on INBreast and Vindr datasets.
Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality mammography datasets for AI development remain limited in both scale and annotation richness, particularly regarding pathological subtype coverage and structured diagnostic reasoning annotations. In this paper, we present MammoExpert, the first mammography dataset with Chain-of-Thought reasoning annotations across three diagnostic phases: (i) primal observation, (ii) factual assessment, and (iii) diagnostic synthesis. Comprising 2,379 mammography images covering 67 WHO-classified histopathology subtypes, each exam provides 42 radiographic features annotated by nine senior radiologists. We evaluate its performance on the breast lesion classification task, demonstrating superior accuracy and reasonability compared to existing classification models. Combining public dataset CBIS-DDSM with MammoExpert yields 7.1\% classification accuracy improvement, while the training model to learn CoT reasoning achieves another 4\% gain on the MammoExpert test set. Similar improvements are observed on INBreast and Vindr datasets, where the full approach yields accuracy gains of 6.9\% and 6.7\%, respectively. MammoExpert can serve as a benchmark for interpretable breast lesion diagnosis through explicit CoT reasoning.