FGVis produces fine-grained, faithful visual explanations by optimizing a pixel mask and clipping gradients during backpropagation to block adversarial evidence without added hyperparameters.
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
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abstract
In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.
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cs.CV 1years
2019 1verdicts
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Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
FGVis produces fine-grained, faithful visual explanations by optimizing a pixel mask and clipping gradients during backpropagation to block adversarial evidence without added hyperparameters.