Ophthalmic images and derivatives such as the retinal nerve fiber layer (RNFL) thickness map are crucial for detecting and monitoring ophthalmic diseases (e.g., glaucoma). For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) linked to functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. The task becomes even more challenging in the presence of image artifacts, which are common due to issues with image acquisition and automated segmentation. In this paper, we propose an artifact-tolerant unsupervised learning framework termed EyeLearn for learning representations of ophthalmic images. EyeLearn has an artifact correction module to learn representations that can best predict artifact-free ophthalmic images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the intra- and inter-image affinities. During training, images are dynamically organized in clusters to form contrastive samples in which images in the same or different clusters are encouraged to learn similar or dissimilar representations, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection using a real-world ophthalmic image dataset of glaucoma patients. Extensive experiments and comparisons with state-of-the-art methods verified the effectiveness of EyeLearn for learning optimal feature representations from ophthalmic images.

翻译：眼球纤维层(RNFL)厚厚图谱等眼球图象和衍生物,如视网膜神经纤维层(RNFL)厚图谱(RNFL)厚图谱等,对于检测和监测眼科疾病(例如青光眼)至关重要。对于通过计算机辅助诊断眼科疾病而言,关键技术是自动从眼科病眼科图象中提取有意义的特征,这些特征可以揭示与功能性视觉损失相关的生物标志(例如RNFL瘦化模式)。然而,从眼眼科图象学图象学学学学将结构性肾脏损伤与人类视力损失联系起来,这是非三角图象学学,主要由于病人之间的解剖图象变化很大。在出现图像成形时,任务变得更加艰巨,因为获取图像和自动分割的问题很常见。在本文中,我们提出一个容取耐性不力的学习框架叫做EyeLearLearn,用于了解眼科图像的表达方式。EyeLearL会有一个修复的校正校正模型,用来从我们测测的表面图象组化到内部和内部学习图象组。