使用多模式大语言模型对色素性皮肤病变进行多类分类
Kimi Iinuma1, Kazuyasu Fujii1, Chisa Nakashima1
1Dermatology, Kindai University Hospital, Osaka, JPN.
Cureus
|August 27, 2025
概括
一个微调的多式大型语言模型 (MMLLM) 从临床照片中准确地分类常见的色素皮肤病变. 这种人工智能工具提供了皮肤镜无法使用的诊断支持.
科学领域:
- 皮肤学中的人工智能
- 医学图像分析
- 计算病理学
背景情况:
- 由于标准照片中的视觉相似性,颜色化皮肤病变存在诊断挑战.
- 多式大型语言模型 (MMLLM) 显示出基于图像的诊断辅助工具的潜力,特别是在资源有限的环境中.
- 皮肤检查虽然有效, 但并非所有人都能使用,
研究的目的:
- 仅使用临床照片来评估微调MMLLM在分类8种常见的色素皮肤疾病的准确性.
- 评估MMLLM作为颜色病变的非皮肤镜诊断支持工具的潜力.
- 为初步皮肤损伤评估提供可扩展的解决方案.
主要方法:
- 一个多式大型语言模型 (InstructBLIP-flan-t5-xl) 通过979张色素皮肤病变图像的精准数据集进行了微调.
- 数据集包括八种病变类型:获得的皮肤黑色细胞瘤,基底细胞癌,乙瘤,恶性黑色素瘤,乳腺瘤,斑块性角质瘤和太阳透镜.
- 模型的性能使用宏平均灵敏度,特异性,F1得分和ROC AUC进行了评估,在训练过程中掩盖了病变标签.
主要成果:
- 微调的MMLLM在验证组中获得了86.0%的宏平均灵敏度,98.2%的特异性和0.86的F1得分.
- 在八个病变类别中,接收器运行特征曲线下的面积 (ROC AUC) 超过了0. 95.
- 恶性黑色素瘤表现高 (94%的灵敏度,0. 98的ROC AUC),而nevus的灵敏度较低 (78%,0. 89的ROC AUC).
结论:
- 精细调整的MMLLM只使用临床照片准确地分类常见的色素皮肤病变.
- 这种人工智能驱动的方法提供了快速的诊断支持,在缺乏皮肤镜的环境中尤其有价值.
- 未来的研究应集中在扩大数据集多样性,进行多中心验证和评估现实世界的临床实用性.
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