170 citations · 517 across the 12 of their papers we have counts for
14 papers
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
Xijun Wang, Santiago López-Tapia, Alice Lucas +3
Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, t…
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model,…
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez +2
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assum…
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning
Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina
Multiple Instance Learning (MIL) is a weakly supervised learning paradigm that is becoming increasingly popular because it requires less labeling effort than fully supervised metho…
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection
Julio Silva-Rodríguez, Adrián Colomer, María A. Sales +2
The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. In recent years, with the development of digitisation devices, the use of computer vis…
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas +2
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radi…
Deep Gaussian Processes for geophysical parameter retrieval
Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina +1
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical…
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
Alice Lucas, Santiago Lopez-Tapia, Rafael Molina +1
While Convolutional Neural Networks (CNNs) trained for image and video super-resolution (SR) regularly achieve new state-of-the-art performance, they also suffer from significant d…
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin +2
In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labell…
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
Santiago López-Tapia, Alice Lucas, Rafael Molina +1
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. Current Video…
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Alice Lucas, Santiago Lopez Tapia, Rafael Molina +1
Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using advers…
Remote Sensing Image Classification with Large Scale Gaussian Processes
Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina +1
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large…
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
Michael Iliadis, Haohong Wang, Rafael Molina +1
In this paper we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteri…
Sparse Bayesian Methods for Low-Rank Matrix Estimation
S. Derin Babacan, Martin Luessi, Rafael Molina +1
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 guar…