34 citations · 39 across the 4 of their papers we have counts for
4 papers
Physics-informed neural networks for blood flow inverse problems
Jeremias Garay, Jocelyn Dunstan, Sergio Uribe +1
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known a…
WarpPINN: Cine-MR image registration with physics-informed neural networks
Pablo Arratia López, Hernán Mella, Sergio Uribe +2
Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish differ…
Inspecting state of the art performance and NLP metrics in image-based medical report generation
Pablo Pino, Denis Parra, Pablo Messina +2
Several deep learning architectures have been proposed over the last years to deal with the problem of generating a written report given an imaging exam as input. Most works evalua…
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
Pablo Messina, Pablo Pino, Denis Parra +7
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context…