模拟五种眼病对人脸识别模型的影响，揭示视觉退化如何扭曲深度学习特征。
Through BrokenEyes: How Eye Disorders Impact Face Detection?
- 用BrokenEyes系统模拟五类常见眼病，生成受干扰的图像输入。
- 发现白内障和青光眼导致特征图严重失真，激活能量与余弦相似度显著下降。
- 适合关注医疗视觉系统鲁棒性与神经影像建模的研究者。
视力障碍影响数百万人的生活，改变视觉信息的处理与感知方式。本文通过BrokenEyes系统，模拟年龄相关性黄斑变性、白内障、青光眼、屈光不正及糖尿病视网膜病变五种常见眼病，分析其对深度学习模型中类神经特征表示的影响。结合人类与非人类数据集，对比正常与疾病特异性训练条件下的模型表现，发现白内障与青光眼导致特征图出现关键性破坏，与这些病症已知的神经处理挑战一致。通过激活能量与余弦相似度等评估指标量化了畸变程度，揭示了退化视觉输入与模型学习表征之间的相互作用。
原文摘要 · Abstract (English)
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.
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