用AI识别皮肤真菌显微图像中的菌丝，准确率高且能避开干扰物。
Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy
- 基于RT-DETR的Transformer模型，可精准定位真菌结构。
- 检测召回率达97.37%，图像级诊断准确率达98.8%。
- 适合临床辅助诊断，尤其擅长处理复杂背景的显微图像。
皮肤真菌病常用氢氧化钾（KOH）显微镜检查，但真菌菌丝识别常受伪影、角质清除不均及观察者差异影响。本研究提出基于RT-DETR架构的Transformer检测框架，对2,540张常规获取的高分辨率KOH图像进行多类标注，明确区分真菌成分与干扰伪影。模型采用保留形态的增强策略，确保细小菌丝结构完整。在独立测试集上，对象级性能优异：召回率0.9737，精确率0.8043，[email protected]达93.56%。图像级诊断表现更佳：敏感性100%，准确率98.8%，所有阳性病例均被正确识别，无漏诊。定性分析显示，该模型在伪影密集区域仍能稳定定位低对比度菌丝。结果表明，AI系统可作为高可靠性的自动化筛查工具，有效连接图像分析与临床决策。
原文摘要 · Abstract (English)
Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based detection framework using the RT-DETR model architecture to achieve precise, query-driven localization of fungal structures in high-resolution KOH images. A dataset of 2,540 routinely acquired microscopy images was manually annotated using a multi-class strategy to explicitly distinguish fungal elements from confounding artefacts. The model was trained with morphology-preserving augmentations to maintain the structural integrity of thin hyphae. Evaluation on an independent test set demonstrated robust object-level performance, with a recall of 0.9737, precision of 0.8043, and an [email protected] of 93.56%. When aggregated for image-level diagnosis, the model achieved 100% sensitivity and 98.8% accuracy, correctly identifying all positive cases without missing a single diagnosis. Qualitative outputs confirmed the robust localization of low-contrast hyphae even in artefact-rich fields. These results highlight that an artificial intelligence (AI) system can serve as a highly reliable, automated screening tool, effectively bridging the gap between image-level analysis and clinical decision-making in dermatomycology.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。