视觉变压器的深度学习将细菌和真菌角膜炎分类,使用不同类型的前部片段图像
Yeo Kyoung Won1, Choong Han Kim2, Jooyoung Jeon2
1Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Computers in biology and medicine
|March 19, 2025
概括
这项研究开发了新的视觉转换器 (ViT) 框架,用于诊断细菌和真菌角膜炎. 将多种图像类型与ViT自我注意力结合起来,显著提高了诊断准确度,显示出临床应用的希望.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 细菌和真菌角膜炎是严重的眼睛感染,需要准确及时诊断.
- 当前的诊断方法在区分细菌和真菌病因方面可能存在局限性.
- 视觉变压器 (ViT) 模型为基于图像的疾病分类提供了潜力.
研究的目的:
- 开发和比较三个新的视觉转换器 (ViT) 框架,用于诊断细菌和真菌角膜炎.
- 为了评估ViT模型的性能,使用不同的前段图像类型 (宽光,裂光,蓝光).
- 评估结合多种图像类型使用自我注意机制以提高诊断准确性的影响.
主要方法:
- 进行了一项回顾性研究,使用细菌和真菌角膜炎患者的前段图像.
- 开发了三个ViT框架,集成一个或多个ViT,使用矢量加法或自我注意来结合图像类型.
- 模型性能通过使用接收器操作特征曲线 (AUROC) 下的区域和精度回调曲线 (AUPRC) 下的区域进行了评估,并进行了交叉验证.
主要成果:
- 这项研究分析了79名患者的图像 (283名宽光,610名裂光,342名蓝光).
- 使用宽束图像的单一ViT模型实现了0.72.7的AUROC.
- 将宽光和裂光图像与自我注意力相结合,产生了最高的AUROC (0.93) 和AUPRC (0.93).
结论:
- 视觉变压器 (ViT) 模型,特别是具有自我注意力,可以有效地改善细菌和真菌角膜炎的诊断.
- 整合多个前段图像类型,如宽光束和裂纹光束,显著提高诊断性能.
- 在ViT框架中的自我注意力机制显示出提高炎诊断的准确性有前途,即使数据集有限.
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