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Found 4 papers, 3 papers with code
Physics-informed neural networks for blood flow inverse problems
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 and scatter measurements are available.
WarpPINN: Cine-MR image registration with physics-informed neural networks
In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of the heart deformation.
Inspecting state of the art performance and NLP metrics in image-based medical report generation
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.
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods.
