Retinal Lesion Detection With Deep Learning Using Image Patches

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


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Exudates and Transudates
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Machine Learning
  • Neural Networks, Computer*
  • Probability
  • ROC Curve
  • Reproducibility of Results
  • Retinal Diseases / diagnostic imaging*