Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration
Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration
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DOI：
10.1364/boe.8.000579
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发表时间：
2017-02-01
影响因子：
3.4
通讯作者：
Chatterjee, Jyotirmoy
中科院分区：
文献类型：
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作者：
Karri, S. P. K.;Chakraborty, Debjani;Chatterjee, Jyotirmoy
我们提出了一种算法，用于识别视网膜光学相干断层扫描（OCT）图像视网膜病变。我们的方法对预训练的卷积神经网络（CNN）GoogLeNet进行微调，以提高其预测能力（与随机初始化训练相比），并在预测过程中识别显着响应，以了解学习到的过滤器特征。我们考虑了一组包含糖尿病性黄斑水肿或干性年龄相关性黄斑变性或无病理学的受试者的数据集。与经典学习相比，经过微调的CNN可以有效地识别病理。我们的算法旨在证明，在非医学图像上训练的模型可以进行微调，以利用有限的训练数据对OCT图像进行分类。(C)2017美国光学学会
We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data. (C) 2017 Optical Society of America