Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.
Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysis.
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DOI：
10.1016/j.compbiomed.2017.01.018
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发表时间：
2017-03-01
影响因子：
7.7
通讯作者：
Bressler NM
中科院分区：
文献类型：
--
作者：
Burlina P;Pacheco KD;Joshi N;Freund DE;Bressler NM
如果不治疗，年龄相关性黄斑变性（AMD）是美国50岁以上人群视力丧失的主要原因。目前，据估计，约有800万美国人患有AMD的中期阶段，其通常没有视觉缺陷的症状。这些个体处于进展到晚期阶段的高风险中，在该晚期阶段中通常可治疗的脉络膜新生血管形式的AMD可发生。仔细监测以检测新生血管形式的发作和及时治疗以及饮食补充可以降低AMD视力丧失的风险，因此，首选的实践模式建议及时识别患有中间阶段的个体。过去应用于眼底成像的自动视网膜图像分析（ARIA）方法依赖于工程化和手工设计的视觉特征。相反，我们详细介绍了使用深度学习的机器学习方法在ARIA和AMD分析问题上的新应用。我们使用从深度卷积神经网络（DCNN）中获得的迁移学习和通用特征。我们解决了临床相关的4级，3级和2级AMD严重程度分类问题。使用来自NIH AREDS数据集和DCNN通用特征的5664张彩色眼底图像，我们获得了机器（79.4%，81.5%，93.4%）与医生分级（75.8%，85.0%，95.2%）的（4-，3-，2-）类分类问题的准确度值。这项研究证明了基于深度通用特征/迁移学习的机器分级在应用于ARIA时的有效性，并且是提供预筛选以识别患有中度AMD的个体的有希望的一步，也是一种工具，可以促进识别这些个体用于旨在开发改进疗法的临床研究。它还证明了计算机和医生分级之间的可比性能。
When left untreated, age-related macular degeneration (AMD) is the leading cause of vision loss in people over fifty in the US. Currently it is estimated that about eight million US individuals have the intermediate stage of AMD that is often asymptomatic with regard to visual deficit. These individuals are at high risk for progressing to the advanced stage where the often treatable choroidal neovascular form of AMD can occur. Careful monitoring to detect the onset and prompt treatment of the neovascular form as well as dietary supplementation can reduce the risk of vision loss from AMD, therefore, preferred practice patterns recommend identifying individuals with the intermediate stage in a timely manner. Past automated retinal image analysis (ARIA) methods applied on fundus imagery have relied on engineered and hand-designed visual features. We instead detail the novel application of a machine learning approach using deep learning for the problem of ARIA and AMD analysis. We use transfer learning and universal features derived from deep convolutional neural networks (DCNN). We address clinically relevant 4-class, 3-class, and 2-class AMD severity classification problems. Using 5664 color fundus images from the NIH AREDS dataset and DCNN universal features, we obtain values for accuracy for the (4-,3-,2-) class classification problem of (79.4%, 81.5%, 93.4%) for machine vs. (75.8%, 85.0%, 95.2%) for physician grading. This study demonstrates the efficacy of machine grading based on deep universal features/transfer learning when applied to ARIA and is a promising step in providing a pre-screener to identify individuals with intermediate AMD and also as a tool that can facilitate identifying such individuals for clinical studies aimed at developing improved therapies. It also demonstrates comparable performance between computer and physician grading.