Rafael Molina Soriano

Professor

Universidad de Granada

Spain

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Professor Rafael Molina Soriano is a faculty member at Universidad de Granada in Spain. His research focuses on advanced methodologies in medical imaging, including deep learning techniques, multiple instance learning, and generative adversarial networks. Recent publications highlight his work on topics such as probabilistic models for histopathological image analysis and real-time video super-resolution. Professor Molina Soriano's contributions are pivotal in the intersection of artificial intelligence and healthcare technology.

Rafael Molina Soriano

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Articles (21)

Probabilistic smooth attention for deep multiple instance learning in medical imaging

LCNet: Lightweight Cycle Network Driven by Physical and Deep Prior for Compressed Sensing

Spatially-Aware Loss Functions for GAN-Driven Super-Resolution

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning

An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection

Real-Time Lightweight Video Super-Resolution with RRED-based Perceptual Constraint

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD

Focused active learning for histopathological image classification

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

The CrowdGleason dataset: Learning the Gleason grade from crowds and experts

Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification

Probabilistic Attention Based on Gaussian Processes for Deep Multiple Instance Learning

Probabilistic fusion of crowds and experts for the search of gravitational waves

Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection *

Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection

Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO

Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification

Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

Proportion constrained weakly supervised histopathology image classification

Collaborators (4)

Pablo Ruiz Matarán

Kjersti Engan

Aggelos K Katsaggelos

Javier Mateos

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