Anna introduced a new mathematical approach for dimensionality reduction that we incorporated into loss functions to augment target information and improve performance.
This is the summary publication of the REFUGE challenge on glaucoma detection in color fundus photographs.
We applied CycleGANs to improve realism in ultrasound simulation based on ray-casting methods.
We used epistemic uncertainty estimates to discover potential abnormalities in diseased OCT scans. The uncertainty maps are obtained by a Bayesian U-Net trained on healthy OCT scans with weak labels of the retinal layers.
We introduced a hybrid red lesion detection model based on a combination of deep learning based features and hand crafted descriptors.
We develop a fractal based model for detecting proliferative diabetic retinopathy cases from fundus pictures.
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 explored and evaluated different feature extraction techniques in the context of retinal blood segmentation with SVMs.