2,280 research outputs found
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Diabetic eye disease is one of the fastest growing causes of preventable
blindness. With the advent of anti-VEGF (vascular endothelial growth factor)
therapies, it has become increasingly important to detect center-involved
diabetic macular edema (ci-DME). However, center-involved diabetic macular
edema is diagnosed using optical coherence tomography (OCT), which is not
generally available at screening sites because of cost and workflow
constraints. Instead, screening programs rely on the detection of hard exudates
in color fundus photographs as a proxy for DME, often resulting in high false
positive or false negative calls. To improve the accuracy of DME screening, we
trained a deep learning model to use color fundus photographs to predict
ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds
to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal
specialists had similar sensitivities (82-85%), but only half the specificity
(45-50%, p<0.001 for each comparison with model). The positive predictive value
(PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by
the retinal specialists. In addition to predicting ci-DME, our model was able
to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI:
0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The
ability of deep learning algorithms to make clinically relevant predictions
that generally require sophisticated 3D-imaging equipment from simple 2D images
has broad relevance to many other applications in medical imaging
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.Comment: Accepted as a conference paper at IEEE EMBC, 201
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