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一种可解释的无监督学习方法,用于在角膜in vivo共聚焦显微镜图像上的异常检测

Ningning Tang¹, Qi Chen¹, Yunyu Meng¹

¹Guangxi Key Laboratory of Eye Health and Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology and Research Center of Ophthalmology, Guangxi Academy of Medical Sciences and Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.

Frontiers in bioengineering and biotechnology

|June 23, 2025

概括

一种新的基于变压器的方法可以在没有先前疾病知识的情况下准确地检测出在生物共聚焦显微镜图像中的角膜异常. 这种方法提高了诊断效率,减少了对标记病理样本的需求.

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

在生物共聚焦显微镜 (IVCM) 对角膜疾病的评估至关重要.
鉴于复杂的结构,将病态与正常的角膜特征区分开来是一项挑战.
现有的异常检测方法在各种角膜病理中缺乏通用性.

研究的目的:

为IVCM图像开发基于变压器的无监督异常检测方法.
为了识别角膜异常,而没有事先了解特定疾病特征.
提高角膜成像中异常检测的准确性和通用性.

主要方法:

提出了一个由三个子模块组成的方法:EfficientNet,多尺度特征融合网络和变压器网络.
在四个角膜层的正常IVCM图像上专门训练模型.
在内部和外部数据集上评估性能,与AnoGAN,G2D和DRAEM进行比较.

主要成果:

实现的ROC曲线下的面积为0.933 (内部) 和0.917 (外部).
在准确性和通用性方面超过现有方法 (AnoGAN,G2D,DRAEM).
在正常图像和病态图像之间有统计学意义的区别 (p < 0.001).

结论:

为IVCM图像提供了一个高效和可解释的无监督异常检测模型.
有效地识别角膜异常而不需要标记病理样本.
提高查效率,降低注释成本,并显示出可扩展的智能角膜疾病诊断的潜力.

关键词:

查角膜疾病查角膜疾病查深度学习 (DL) 是指深度学习.可解释的人工智能在体内共聚焦显微镜 (IVCM)没有监督的异常检测检测.

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