Journal Papers
- F. Pérez-Bueno, M. Vega, J. Mateos, R. Molina, and A.K. Katsaggelos, “Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors”, Sensors, vol. 20, 5308, September 2020.
DOIPDF
- S. López-Tapia, and N. Pérez de la Blanca, “Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution”, IEEE Transactions on Image Processing, vol. 30, 4747 - 4759, April 2021.
DOIPDF
- M. López-Pérez, L. García, C. Benítez, and R. Molina, “A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, 3875 - 3890, May 2021.
DOIPDF
- M. López-Pérez, M. Amgad, P. Morales-Álvarez, P. Ruiz, L.A.D. Cooper, R. Molina, and A.K. Katsaggelos, “Learning from crowds in digital pathology using scalable variational Gaussian processes”, Scientific Reports, no. 11, 11612, June 2021.
DOIPDFCode
- S. López-Tapia, R. Molina, and A. K. Katsaggelos, “Deep learning approaches to inverse problems in imaging: Past, present and future”, Digital Signal Processing, vol. 119, 103285, October 2021.
DOIPDF
- F. Pérez-Bueno, M. Vega, M.A. Sales, J. Aneiros-Fernández, V. Naranjo, R. Molina, and A.K. Katsaggelos, “Blind Color Deconvolution, Normalization and Classification of Histological Images Using General Super Gaussian Priors and Bayesian Inference”, Computer Methods and Programs in Biomedicine, vol. 211, 106453, November 2021.
DOIPDF
- A. Schmidt, J. Silva-Rodríguez, R. Molina, and V. Naranjo, “Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning”, IEEE Access, vol. 10, 9763-9773, January 2022.
DOIPDF
- P. Morales-Álvarez, P. Ruiz, S. Coughlin, R. Molina, and A.K. Katsaggelos, “Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, 1534-1551, March 2022.
DOIPDF
- F. Pérez-Bueno, J.G. Serra, M. Vega, J. Mateos, R. Molina, and A. K. Katsaggelos, “Bayesian K-SVD for H&E blind color deconvolution. Applications to stain normalization, data augmentation, and cancer classification”, Computerized Medical Imaging and Graphics, vol. 97, 102048, April 2022.
DOIPDF
- N. Kanwal, F. Pérez-Bueno, A. Schmidt, K. Engan, and R. Molina, “The devil is in the details: Whole Slide Image acquisition and processing for artifact detection, color variation, and data augmentation. A review.”, IEEE Access, vol. 10, 58821-58844, May 2022.
DOIPDF
- F. Pérez-Bueno, L. García, G. Maciá-Fernandez, and R. Molina, “Leveraging a Probabilistic PCA model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection”, IEEE/ACM Transactions on Networking, vol. 30, no. 3, 1217-1229, June 2022.
DOIPDF
- J. Silva-Rodríguez, A. Schmidt, M. A. Sales, R. Molina, and V. Naranjo, “Proportion constrained weakly supervised histopathology image classification”, Computers in Biology and Medicine, December 2022.
DOIPDF
- M. López-Pérez, P. Morales-Álvarez, L. A. D. Cooper, R. Molina, and A. K. Katsaggelos, “Deep Gaussian Processes for classification with multiple noisy annotators. Application to breast cancer tissue classification”, IEEE Access, vol. 11, 6922 - 6934, January 2023.
DOIPDFCode
- P. Ruiz, P. Morales-Álvarez, S. Coughlin, R. Molina, and A.K. Katsaggelos, “Probabilistic fusion of crowds and experts for the search of gravitational waves”, Knowledge-based systems, vol. 261, 110183, February 2023.
DOIPDF
- M. A. Ruiz-Fresneda, A. Gijón, and P. Morales-Álvarez, “Bibliometric analysis of the global scientific production on machine learning applied to different cancer types”, Environmental Science and Pollution Research, vol. 30, August 2023.
DOIPDF
- R. del Amor, J. Pérez-Cano, M. López-Pérez, L. Terradez, J. Aneiros-Fernandez, S. Morales, J. Mateos, R. Molina, and V. Naranjo, “Annotation Protocol and Crowdsourcing Multiple Instance Learning Classification of Skin Histological Images: the CR-AI4SkIN Dataset”, Artificial Intelligence in Medicine, vol. 145, 102686, November 2023.
DOIPDFCode
- P. Morales-Álvarez, A. Schmidt, J.M. Hernández-Lobato, and R. Molina, “Introducing instance correlation in multiple instance learning. Application to cancer detection on histopathological images”, Pattern Recognition, vol. 146, February 2024.
DOIPDF
- F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu, R. Molina, and A.K. Katsaggelos, “Hyperbolic Secant Representation of the Logistic Function: Application to Probabilistic Multiple Instance Learning for CT Intracranial Hemorrhage Detection”, Artificial Intelligence, June 2024.
DOIPDF
- A. Schmidt, P. Morales-Álvarez, and R. Molina, “Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, August 2024.
DOIPDF
Conference Papers
- P. Morales-Alvarez, D. Hernández-Lobato, R. Molina, and J.M. Hernández-Lobato, “Activation-level uncertainty in deep neural networks” in International Conference on Learning Representations (ICLR), May 2021.
DOIPDF
- Y. Wu, A. Schmidt, E. Hernández-Sánchez, R. Molina, and A. K. Katsaggelos, “Combining Attention-based Multiple Instance Learning and Gaussian Processes for CTHemorrhage Detection” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), edited by Caroline Essert, September 2021.
DOIPDF
- S. Yang, F. Pérez-Bueno, F. M. Castro-Macías, R. Molina, and A. K. Katsaggelos, “Deep Bayesian Blind Color Deconvolution of Histological Images” in IEEE International Conference on Image Processing (ICIP), edited by IEEE, 710-714, October 2023.
DOIPDFCode
- Y. Wu, F. M. Castro-Macías, P. Morales-Álvarez, R. Molina, and A.K. Katsaggelos, “Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection” in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), edited by Springer, vol. 14224, October 2023.
DOIPDFCode
- S. López-Tapia, J. Mateos, R. Molina, and A.K. Katsaggelos, “Deep robust image restoration using the Moore-Penrose blur inverse” in 2023 IEEE International Conference on Image Processing (ICIP 2023), 775-779, October 2023.
DOICode
- A. Schmidt, P. Morales-Álvarez, and R. Molina, “Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation” in IEEE/CVF International Conference on Computer Vision (ICCV) 2023, 21097-21106, October 2023.
DOIPDF
Theses
- P. Morales-Álvarez, Development of New Machine Learning Models Based on Gaussian Processes. Applications to Remote Sensing and Astrophysics., Universidad de Granada, October 2020. Supervised by: Rafael Molina and Aggelos K. Katsaggelos.
PDF
- S. López-Tapia, Deep Learning Models for Image and Video Processing and Classification, Universidad de Granada, January 2021. Supervised by: Rafael Molina and Aggelos K. Katsaggelos.
PDF
- M. López-Pérez, Probabilistic Methods for Image and Signal Classification. Applications to Medicine and Volcanology, Universidad de Granada, July 2022. Supervised by: R. Molina and A. K. Katsaggelos.
PDF
- F. Pérez-Bueno, Improvement, Classification and Interpretation of Cancer Histological Images Using Probabilistic Models, Universidad de Granada, November 2022. Supervised by: R. Molina and V. Naranjo.
PDF
- A. Schmidt, Probabilistic Deep Learning for Histopathological Images: Overcoming the Labeling Bottleneck of Computer-Aided Diagnosis, Universidad de Granada, February 2024. Supervised by: R. Molina and P. Morales-Álvarez.
PDF