deep learning

With no other methodological innovation than a carefully designed training, our ResNet model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007 test images from different public datasets, which is in line or even better than what other more complex deep learning models reported in the literature.

We train the first deep learning model for segmenting brain arteries from 3D rotational angiographies in cases with brain arterio-venous malformations.

We experimentally validate whether using coarse-to-fine models instead of one-stage models is appropriate or not for segmenting the optic disc and the optic cup in color fundus images. We observed that models trained with the right amount of data can perform much better than coarse-to-find approaches.

Cuando aplicamos inteligencia artificial en medicina, solemos seguir uno de dos enfoques: o bien utilizar modelos que ya existen para resolver un problema nuevo, o bien proponer un modelo nuevo para resolver problemas que ya existen. En todos los …

We developed a deep learning approach that leverages vessel segmentations from color fundus photographs to improve FAZ segmentation in FA images.

We present ReSenseNet, a 3D to 2D deep neural network that is able to predict microperimetry maps from OCT images.

We develop a deep neural network that automatically extracts contextual features from patches in sketches, trained with 3D models rendered with non-photorealistic techniques. Our method is able to find dense correspondences between real world sketches!

We developed a deep learning based pipeline to segment the lumen boundary in IVUS datasets based on multiframes inputs.

This is the summary publication of the AGE challenge on angle closure glaucoma detection from OCT scans of the anterior segment.

We introduced a fully automated approach to segment the photorceptor layer, evaluate its thickness and track potential disruptions using an ensemble of deep neural networks.