Deep Learning

La charla tiene como objetivo abordar las oportunidades y desafíos de la digitalización en salud en el contexto actual de la …

La charla propone un viaje de ida y vuelta entre la frontera científica y el impacto social. En la primera parte revisaremos los …

En esta charla hablaremos de Retinar, un proyecto de investigación y desarrollo del Grupo Yatiris del Instituto PLADEMA que utiliza …

This paper presents a computational tool for guiding intracranial aneurysm treatment by aligning preoperative simulation data with real-time X-Ray imaging, enabling more accurate flow diverter deployment. The method was validated on patient data, showing its potential to improve decision-making during procedures by visualizing the device’s current and planned positions.

Los algoritmos de clasificación a una clase (one-class classification) fueron originalmente pensados para tareas no-supervisadas tales …

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 …

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