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Invest Ophthalmol Vis Sci. 2018 Jan 1;59(1):590-596. doi: 10.1167/iovs.17-22721.

Retinal Lesion Detection With Deep Learning Using Image Patches.

Lam C1,2, Yu C3, Huang L4, Rubin D1,4,5.

Author information

1
Department of Biomedical Data Science, Stanford University, Stanford, California, United States.
2
Department of Ophthalmology, Santa Clara Valley Medical Center, San Jose, California, United States.
3
Stanford University School of Medicine, Stanford, California, United States.
4
Department of Ophthalmology, Stanford University School of Medicine, Stanford, California, United States.
5
Department of Radiology, Stanford University School of Medicine, Stanford, California, United States.

Abstract

Purpose:

To develop an automated method of localizing and discerning multiple types of findings in retinal images using a limited set of training data without hard-coded feature extraction as a step toward generalizing these methods to rare disease detection in which a limited number of training data are available.

Methods:

Two ophthalmologists verified 243 retinal images, labeling important subsections of the image to generate 1324 image patches containing either hemorrhages, microaneurysms, exudates, retinal neovascularization, or normal-appearing structures from the Kaggle dataset. These image patches were used to train one standard convolutional neural network to predict the presence of these five classes. A sliding window method was used to generate probability maps across the entire image.

Results:

The method was validated on the eOphta dataset of 148 whole retinal images for microaneurysms and 47 for exudates. A pixel-wise classification of the area under the curve of the receiver operating characteristic of 0.94 and 0.95, as well as a lesion-wise area under the precision recall curve of 0.86 and 0.64, was achieved for microaneurysms and exudates, respectively.

Conclusions:

Regionally trained convolutional neural networks can generate lesion-specific probability maps able to detect and distinguish between subtle pathologic lesions with only a few hundred training examples per lesion.

PMID:
29372258
PMCID:
PMC5788045
DOI:
10.1167/iovs.17-22721
[Indexed for MEDLINE]
Free PMC Article

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