LCFP-14M: A High-quality Large-scale Color Fundus Photography Images Dataset with Eleven Types of Annotations

More and more human beings are suffering from ophthalmic diseases, and automatic analysis of images from Color Fundus Photography (CFP) using deep learning algorithms can improve the accuracy and prevalence of early screening for ophthalmic diseases. However, the performance of deep learning models is highly dependent on the training data, and the currently available retinal image datasets still have limitations such as scarce total data and poor disease diversity. Therefore, we extensively collected and integrated the CFP publicly available datasets, resulting in the construction LCFP-14M dataset containing 13,718,610 retinal fundus images with a total of 150,904,710 annotations, which is one of the largest datasets and covers the widest range of ophthalmic diseases at present.

当下越来越多的人群罹患眼部疾病，利用深度学习算法对彩色眼底摄影（Color Fundus Photography, CFP）图像进行自动分析，可提升眼部疾病早期筛查的准确率与筛查普及率。然而，深度学习模型的性能高度依赖训练数据，当前可用的视网膜图像数据集仍存在总数据量稀缺、疾病多样性不足等局限。为此，我们广泛收集并整合了公开可用的CFP数据集，构建了包含13718610张视网膜眼底图像、共计150904710个标注的LCFP-14M数据集，该数据集是目前规模最大、覆盖眼部疾病种类最广的数据集之一。

搜集汇总

背景与挑战

背景概述

LCFP-14M是一个大规模高质量彩色眼底摄影图像数据集，包含13,718,610张图像和150,904,710个标注，覆盖目前最广泛的眼科疾病类型，旨在解决现有数据集数据量不足和疾病多样性有限的问题。