We used SVMs to filter out false positive detections causes by single subject VBM when applied for discovering abnormalities in MRI scans.
We designed a method to summarize hemodynamic parameters obtained by 0D simulations so that they can be applied for glaucoma detection. We observed certain correlation between glaucoma and these hemodynamic features.
We developed a simple linear regression model that is able to estimate the hyperparameters of a fully-connected CRF model for blood vessel segmentation in fundus images.
We develop a fractal based model for detecting proliferative diabetic retinopathy cases from fundus pictures.
We use pretrained VGG-S and OverFeat architectures as feature extractors for glaucoma detection in fundus pictures. We were able to get almost 0.8 AUC without fine-tuning the networks!
We present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.
We explored and evaluated different feature extraction techniques in the context of IVUS segmentation.
We introduced a discriminatively trained fully-connected conditional random field model for blood vessel segmentation in retinal images.
We introduced a model for Arabidopsis thaliana root segmentation based on CRFs.