发表机构
Dr. Robot Inc.; Toronto Metropolitan University; Skinopathy Inc.（机器人博士公司; 多伦多都会大学; 皮肤病理公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了基于EfficientNet-B0迁移学习与Grad-CAM的可解释四类痤疮分级分类器，在ACNE04数据集上取得高准确率与宏F1值，提供跨平台开源实现，为临床验证提供参考。
AI 中文摘要
寻常痤疮影响大多数青少年及众多成年人，准确的严重程度分级可指导治疗、监测及临床试验终点，但采用研究者全局评估或Hayashi标准的人工评估存在评分者间差异及成像条件不一致的局限。本研究基于Hayashi标准开发了四类痤疮严重程度分类器，采用在ImageNet上预训练的EfficientNet-B0模型进行迁移学习，使用AdamW优化器、几何与光度增强技术，并基于验证集宏F1值选择检查点，在含2983张标注图像的公开ACNE04数据集上对模型进行微调。在分层划分的15%留存测试集上，该分类器达到93.5%的准确率与94.4%的宏F1值，各类别F1分数介于0.92至0.97之间，83%的错误发生在相邻等级间，二次加权Cohen卡帕值为0.956，95%置信区间为0.935至0.973，自举置信区间表明性能稳定。来自最终卷积块的Grad-CAM可视化结果聚焦于临床相关的面部区域，包括额头、脸颊与下巴。完整流程以Python（使用PyTorch与timm）和MATLAB R2026a两种功能等效的开源实现提供，软件包含面向临床医生的推理界面及支持无需专用预训练权重包运行的备用主干选项。这些结果表明，轻量级迁移学习可提供准确、均衡且可解释的痤疮严重程度分级，同时为未来前瞻性及设备分层的临床验证提供可复现的跨平台参考。
英文摘要
Acne vulgaris affects most adolescents and many adults. Accurate severity grading guides treatment, monitoring, and clinical trial endpoints, but manual assessment using the Investigator's Global Assessment or Hayashi criteria is limited by inter-rater variability and inconsistent imaging conditions. We developed a four-class acne severity classifier based on the Hayashi criteria using transfer learning with an ImageNet-pretrained EfficientNet-B0 model. The model was fine-tuned on the public ACNE04 dataset of 2,983 labeled images using AdamW optimization, geometric and photometric augmentation, and checkpoint selection based on validation macro-F1. On a held-out stratified 15 percent test set, the classifier achieved 93.5 percent accuracy and 94.4 percent macro-F1, with per-class F1 scores from 0.92 to 0.97. Eighty-three percent of errors occurred between adjacent grades. Quadratic-weighted Cohen's kappa was 0.956, with a 95 percent confidence interval of 0.935 to 0.973. Bootstrap confidence intervals indicated stable performance. Grad-CAM visualizations from the final convolutional block focused on clinically relevant facial regions, including the forehead, cheeks, and chin. The complete pipeline is provided as functionally equivalent open-source implementations in Python using PyTorch and timm, and in MATLAB R2026a. The software includes a clinician-facing inference interface and a fallback backbone option that supports operation without specialized pretrained-weight packages. These results show that lightweight transfer learning can provide accurate, balanced, and interpretable acne severity grading while offering a reproducible cross-platform reference for future prospective and device-stratified clinical validation.
Comments22 pages, 7 figures, 7 tables
