TCM-Tongue：一个用于AI辅助中医诊断的带病理标注的标准化舌象图像数据集
TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
AI总结：
针对中医舌诊缺乏大规模标准化标注数据的问题，本文构建含6,719张图像、20类病理标注的TCM-Tongue数据集，并经九种深度学习模型基准验证。
摘要：
中医（TCM）舌诊虽具有临床价值，但由于主观解读和成像方案不一致而面临标准化挑战，此外还缺乏用于AI开发的大规模标注数据集。为弥补这一空白，我们提出首个面向AI驱动中医舌诊的专用数据集，包含6,719张在标准化条件下采集的高质量图像，并标注了20种病理症状类别（平均每张图像有2.54个经临床验证的标签，且全部由持证中医从业者核验）。该数据集支持多种标注格式（COCO、TXT、XML），以实现广泛可用性，并已使用九种深度学习模型（YOLOv5/v7/v8变体、SSD和MobileNetV2）进行基准测试，以证明其在AI开发中的效用。该资源为推进可靠的中医计算工具提供了关键基础，弥合了长期阻碍该领域进展的数据短缺，并通过标准化、高质量的诊断数据促进AI融入研究与临床实践。
英文摘要：
Traditional Chinese medicine (TCM) tongue diagnosis, while clinically valuable, faces standardization challenges due to subjective interpretation and inconsistent imaging protocols, compounded by the lack of large-scale, annotated datasets for AI development. To address this gap, we present the first specialized dataset for AI-driven TCM tongue diagnosis, comprising 6,719 high-quality images captured under standardized conditions and annotated with 20 pathological symptom categories (averaging 2.54 clinically validated labels per image, all verified by licensed TCM practitioners). The dataset supports multiple annotation formats (COCO, TXT, XML) for broad usability and has been benchmarked using nine deep learning models (YOLOv5/v7/v8 variants, SSD, and MobileNetV2) to demonstrate its utility for AI development. This resource provides a critical foundation for advancing reliable computational tools in TCM, bridging the data shortage that has hindered progress in the field, and facilitating the integration of AI into both research and clinical practice through standardized, high-quality diagnostic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
