Abstract
Vision disorders significantly impact millions of lives, altering how visual information is processed and perceived. In this work, a computational framework was developed using the BrokenEyes system to simulate five common eye disorders: Age-related macular degeneration, cataract, glaucoma, refractive errors, and diabetic retinopathy and analyze their effects on neural-like feature representations in deep learning models. Leveraging a combination of human and non-human datasets, models trained under normal and disorder-specific conditions revealed critical disruptions in feature maps, particularly for cataract and glaucoma, which align with known neural processing challenges in these conditions. Evaluation metrics such as activation energy and cosine similarity quantified the severity of these distortions, providing insights into the interplay between degraded visual inputs and learned representations.
Abstract (translated)
视力障碍显著影响着数百万人的生活，改变了视觉信息的处理和感知方式。在这项工作中，研究人员利用BrokenEyes系统开发了一个计算框架，以模拟五种常见的眼部疾病：年龄相关性黄斑变性、白内障、青光眼、屈光不正以及糖尿病视网膜病变，并分析这些疾病对深度学习模型中神经元特征表示的影响。通过结合使用人类和非人类数据集，在正常条件及特定疾病的条件下训练的模型揭示了特征图的重要破坏，尤其是在白内障和青光眼中，这与这些病症下的已知神经处理挑战相吻合。激活能量和余弦相似度等评估指标量化了这些扭曲的程度，为受损视觉输入与学习表示之间的相互作用提供了见解。
URL
https://arxiv.org/abs/2602.23212
