用少量标注数据训练眼科疾病分类模型，提升早期诊断效率。
Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of Eye
- 分阶段训练：先学健康与糖尿病视网膜病变，再扩展到青光眼
- 在有限数据下实现91%整体准确率，适合医疗标注资源少的场景
- 通过扰动分析揭示模型决策关键特征，增强可解释性
全球眼病患病率持续上升，早期发现与及时干预对防止视力损伤、改善预后至关重要。本文提出一种名为受限数据下的类别扩展（Class Extension with Limited Data, CELD）的新框架，用于训练视网膜眼底图像分类器。模型首先学习健康与糖尿病视网膜病变（DR）类别的相关特征，随后微调以实现健康、DR和青光眼三类分类任务。该渐进式策略在标注数据稀缺的情况下仍能有效提升分类能力。同时，采用扰动方法识别影响模型决策的关键输入特征。在公开数据集上，整体准确率达到91%。
原文摘要 · Abstract (English)
The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train a classifier to categorize retinal fundus images. The classifier is initially trained to identify relevant features concerning Healthy and Diabetic Retinopathy (DR) classes and later fine-tuned to adapt to the task of classifying the input images into three classes: Healthy, DR, and Glaucoma. This strategy allows the model to gradually enhance its classification capabilities, which is beneficial in situations where there are only a limited number of labeled datasets available. Perturbation methods are also used to identify the input image characteristics responsible for influencing the models decision-making process. We achieve an overall accuracy of 91% on publicly available datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。