Abstract
In recent years, the incidence of vision-threatening eye diseases has risen dramatically, necessitating scalable and accurate screening solutions. This paper presents a comprehensive study on deep learning architectures for the automated diagnosis of ocular conditions. To mitigate the "black-box" limitations of standard convolutional neural networks (CNNs), we implement a pipeline that combines deep feature extraction with interpretable image processing modules. Specifically, we focus on high-fidelity retinal vessel segmentation as an auxiliary task to guide the classification process. By grounding the model's predictions in clinically relevant morphological features, we aim to bridge the gap between algorithmic output and expert medical validation, thereby reducing false positives and improving deployment viability in clinical settings.
Abstract (translated)
近年来，威胁视力的眼病发病率急剧上升，亟需可扩展且准确的筛查解决方案。本文介绍了一项关于用于自动诊断眼部状况的深度学习架构的全面研究。为了解决标准卷积神经网络（CNN）的“黑箱”局限性，我们实现了一个结合了深度特征提取与可解释图像处理模块的工作流程。具体而言，我们将高保真视网膜血管分割作为辅助任务来指导分类过程。通过将模型预测建立在临床相关的形态学特征上，我们旨在弥合算法输出和专家医学验证之间的差距，从而减少假阳性，并提高其在临床环境中的部署可行性。
URL
https://arxiv.org/abs/2512.10608
