用血管分割辅助分类，提升眼病诊断的准确与可信度。
Robust Multi-Disease Retinal Classification via Xception-Based Transfer Learning and W-Net Vessel Segmentation
- 以Xception模型提取特征，结合血管分割引导分类决策。
- 通过可解释的血管结构分析，降低误诊率，增强临床可用性。
- 适合关注医学AI可解释性的研究者与临床部署团队。
近年来，威胁视力的眼病发病率急剧上升，亟需高效精准的筛查方案。本文系统研究了深度学习架构在眼部疾病自动化诊断中的应用。为克服标准卷积神经网络（CNN）的“黑箱”缺陷，我们构建了一套结合深层特征提取与可解释图像处理模块的流程。重点聚焦高保真视网膜血管分割，作为辅助任务指导分类过程。通过将模型预测锚定在临床上相关的形态学特征上，旨在弥合算法输出与专家医学判断之间的差距，减少假阳性，提升模型在真实临床环境中的可部署性。
原文摘要 · Abstract (English)
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.
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