no code implementations • 6 Apr 2024 • Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper, Lee A. Newberg, Andinet Enquobahrie, Aggelos K. Katsaggelos, Rafael Molina
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
no code implementations • 15 Mar 2024 • Xijun Wang, Santiago López-Tapia, Alice Lucas, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
1 code implementation • Computerized Medical Imaging and Graphics 2023 • Neel Kanwal, Miguel López-Pérez, Umay Kiraz, Tahlita C.M. Zuiverloon, Rafael Molina, Kjersti Engan
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Ranked #1 on Artifact Detection on HistoArtifacts
no code implementations • 30 Oct 2023 • Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina
We show that our model achieves better results than other state-of-the-art probabilistic MIL methods.
no code implementations • ICCV 2023 • Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions.
1 code implementation • 18 Jul 2023 • Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown.
1 code implementation • 8 Feb 2023 • Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Moreover, its probabilistic nature guarantees robustness to overfitting on small datasets and uncertainty estimations for the predictions.
no code implementations • 2 Feb 2023 • Fernando Pérez-Bueno, Luz García, Gabriel Maciá-Fernández, Rafael Molina
It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection.
1 code implementation • 21 May 2021 • Julio Silva-Rodríguez, Adrián Colomer, María A. Sales, Rafael Molina, Valery Naranjo
The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies.
no code implementations • 16 Apr 2021 • Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas, Rafael Molina, Gustau Camps-Valls
Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations.
no code implementations • ICLR 2021 • Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato
Current approaches for uncertainty estimation in deep learning often produce too confident results.
no code implementations • 7 Dec 2020 • Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina, Gustau Camps-Valls
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval.
no code implementations • 30 Dec 2019 • Alice Lucas, Santiago Lopez-Tapia, Rafael Molina, Aggelos K. Katsaggelos
We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
no code implementations • 5 Nov 2019 • Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin, Rafael Molina, Aggelos K. Katsaggelos
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
no code implementations • 2 Jul 2019 • Santiago López-Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
no code implementations • 14 Jun 2018 • Alice Lucas, Santiago Lopez Tapia, Rafael Molina, Aggelos K. Katsaggelos
Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.
no code implementations • 2 Oct 2017 • Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
1 code implementation • 8 May 2016 • Michael Iliadis, Haohong Wang, Rafael Molina, Aggelos K. Katsaggelos
In this paper we propose an iterative method to address the face identification problem with block occlusions.
no code implementations • 25 Feb 2011 • S. Derin Babacan, Martin Luessi, Rafael Molina, Aggelos K. Katsaggelos
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications.