IIIT Hyderabad Publications |
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Detection of Neovascularization in Retinal Images using Semi-Supervised LearningAuthors: Pujitha Appan K.,Jahnavi Gamalapati S.,Jayanthi Sivaswamy Conference: IEEE International Symposium on Biomedical Imaging 2017 (ISBI-2017 2017) Location Melbourne, Australia Date: 2017-04-18 Report no: IIIT/TR/2017/24 AbstractRetinal Neovascularization (NV) is a critical stage of Diabetic Retinopathy (DR) and its detection is important to prevent blindness. Existing fully supervised frameworks typically take a patch-based approach and report good results only on limited number of images due to sparsity of annotated data. We propose a patch-based semi-supervised framework which paves the way for including unlabeled data in training. In this framework, NV patches are modeled using oriented energy and vesselness based features. These features are fused within a co-training based semi-supervised framework by using neighborhood information in feature space. Rule-based criteria on patch-level neovascularity scores is used to derive the final image-level decision. The proposed approach was evaluated on 1 private and 3 public datasets, both at patch and image level detection on nearly 200,000 patches. An AUC of 0.985 with sensitivity of 96.2% at specificity of 92.6% was obtained for abnormality detection at patch-level, while at the image-level, a sensitivity of 96.76% at a specificity of 91.85% were obtained. The achieved performance on a large number of patches indicates the robustness of our approach. Full paper: pdf Centre for Visual Information Technology |
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