An explainable unsupervised learning approach for anomaly detection on corneal in vivo confocal microscopy images.

IF 4.3 3区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Frontiers in Bioengineering and Biotechnology Pub Date : 2025-06-06 eCollection Date: 2025-01-01 DOI:10.3389/fbioe.2025.1576513
Ningning Tang, Qi Chen, Yunyu Meng, Daizai Lei, Li Jiang, Yikun Qin, Xiaojia Huang, Fen Tang, Shanshan Huang, Qianqian Lan, Qi Chen, Lijie Huang, Rushi Lan, Xipeng Pan, Huadeng Wang, Fan Xu, Wenjing He
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引用次数: 0

Abstract

Background: In vivo confocal microscopy (IVCM) is a crucial imaging modality for assessing corneal diseases, yet distinguishing pathological features from normal variations remains challenging due to the complex multi-layered corneal structure. Existing anomaly detection methods often struggle to generalize across diverse disease manifestations. To address these limitations, we propose a Transformer-based unsupervised anomaly detection method for IVCM images, capable of identifying corneal abnormalities without prior knowledge of specific disease features.

Methods: Our method consists of three submodules: an EfficientNet network, a Multi-Scale Feature Fusion Network, and a Transformer Network. A total of 7,063 IVCM images (95 eyes) were included for analysis. The model was trained exclusively on normal IVCM images to capture and differentiate structural variations across four distinct corneal layers: epithelium, sub-basal nerve plexus, stroma, and endothelium. During inference, anomaly scores were computed to distinguish pathological from normal images. The model's performance was evaluated on both internal and external datasets, and comparative analyses were conducted against existing anomaly detection methods, including generative adversarial networks (AnoGAN), generate to detect anomaly model (G2D), and discriminatively trained reconstruction anomaly embedding model (DRAEM). Additionally, explainable anomaly maps were generated to enhance the interpretability of model decisions.

Results: The proposed method achieved an the areas under the receiver operating characteristic curve of 0.933 on internal validation and 0.917 on an external test dataset, outperforming AnoGAN, G2D, and DRAEM in both accuracy and generalizability. The model effectively distinguished normal and pathological images, demonstrating statistically significant differences in anomaly scores (p < 0.001). Furthermore, visualization results indicated that the detected anomalous regions corresponded to morphological deviations, highlighting potential imaging biomarkers for corneal diseases.

Conclusion: This study presents an efficient and interpretable unsupervised anomaly detection model for IVCM images, effectively identifying corneal abnormalities without requiring labeled pathological samples. The proposed method enhances screening efficiency, reduces annotation costs, and holds great potential for scalable intelligent diagnosis of corneal diseases.

一种可解释的无监督学习方法用于角膜体内共聚焦显微镜图像的异常检测。
背景:体内共聚焦显微镜(IVCM)是评估角膜疾病的重要成像方式,但由于角膜复杂的多层结构,从正常变化中区分病理特征仍然具有挑战性。现有的异常检测方法往往难以概括不同的疾病表现。为了解决这些限制,我们提出了一种基于transformer的IVCM图像无监督异常检测方法,能够在不事先了解特定疾病特征的情况下识别角膜异常。方法:该方法由三个子模块组成:高效网络、多尺度特征融合网络和变压器网络。共计7063张IVCM图像(95只眼)纳入分析。该模型仅在正常的IVCM图像上进行训练,以捕获和区分四个不同角膜层的结构变化:上皮、基底下神经丛、基质和内皮。在推理过程中,计算异常分数以区分病理和正常图像。在内部和外部数据集上评估了该模型的性能,并与现有的异常检测方法进行了比较分析,包括生成对抗网络(AnoGAN)、生成检测异常模型(G2D)和判别训练重建异常嵌入模型(DRAEM)。此外,生成了可解释的异常图,以增强模型决策的可解释性。结果:该方法在内部验证和外部测试数据集上的受试者工作特征曲线下面积分别为0.933和0.917,在准确性和泛化性方面均优于AnoGAN、G2D和DRAEM。该模型有效区分了正常和病理图像,异常评分差异有统计学意义(p < 0.001)。此外,可视化结果表明,检测到的异常区域与形态学偏差相对应,突出了潜在的角膜疾病成像生物标志物。结论:本研究提出了一种高效且可解释的IVCM图像无监督异常检测模型,无需标记病理样本即可有效识别角膜异常。该方法提高了筛查效率,降低了标注成本,在可扩展的角膜疾病智能诊断中具有很大的潜力。
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来源期刊
Frontiers in Bioengineering and Biotechnology
Frontiers in Bioengineering and Biotechnology Chemical Engineering-Bioengineering
CiteScore
8.30
自引率
5.30%
发文量
2270
审稿时长
12 weeks
期刊介绍: The translation of new discoveries in medicine to clinical routine has never been easy. During the second half of the last century, thanks to the progress in chemistry, biochemistry and pharmacology, we have seen the development and the application of a large number of drugs and devices aimed at the treatment of symptoms, blocking unwanted pathways and, in the case of infectious diseases, fighting the micro-organisms responsible. However, we are facing, today, a dramatic change in the therapeutic approach to pathologies and diseases. Indeed, the challenge of the present and the next decade is to fully restore the physiological status of the diseased organism and to completely regenerate tissue and organs when they are so seriously affected that treatments cannot be limited to the repression of symptoms or to the repair of damage. This is being made possible thanks to the major developments made in basic cell and molecular biology, including stem cell science, growth factor delivery, gene isolation and transfection, the advances in bioengineering and nanotechnology, including development of new biomaterials, biofabrication technologies and use of bioreactors, and the big improvements in diagnostic tools and imaging of cells, tissues and organs. In today`s world, an enhancement of communication between multidisciplinary experts, together with the promotion of joint projects and close collaborations among scientists, engineers, industry people, regulatory agencies and physicians are absolute requirements for the success of any attempt to develop and clinically apply a new biological therapy or an innovative device involving the collective use of biomaterials, cells and/or bioactive molecules. “Frontiers in Bioengineering and Biotechnology” aspires to be a forum for all people involved in the process by bridging the gap too often existing between a discovery in the basic sciences and its clinical application.
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