ChestXLLaMA v1.0: Chain-of-Thought Annotations, Prompts, and Generated Reports for Chest X-ray Reasoning
Published: 31 October 2025| Version 1 | DOI: 10.17632/f6b57wzsn6.1
Contributor:
Parsa MohammadiDescription
This dataset accompanies the paper “ChestXLLaMA: A Chain-of-Thought-Enhanced Vision–Language Model for Clinical Reasoning in Chest X-ray Reporting.” It contains the chain-of-thought (CoT) prompts, teacher-model instructions, and example annotated reports used for training and evaluating the ChestXLLaMA model. Each sample pairs a reference chest X-ray report with a ten-step radiology reasoning sequence generated by expert teacher models. No patient images or protected health information are included; only textual annotations, prompts, and generated reasoning outputs. The data support reproducibility of the model’s reasoning-enhanced training process.
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Institutions
- Amirkabir University of Technology Department of Electrical Engineering
Categories
Radiology, Artificial Intelligence, Machine Learning