Journal Papers
- 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
- 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
- M. López-Pérez, A. Schmidt, Y. Wu, R. Molina, and A.K. Katsaggelos, “Deep Gaussian Processes for Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection”, Computer Methods and Programs in Biomedicine, vol. 219, 106783, June 2022.
DOIPDFCode
- 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
- S. López-Tapia, J. Mateos, R. Molina, and A.K. Katsaggelos, “Learning Moore-Penrose Based Residuals for Robust Non-Blind Image Deconvolution”, Digital Signal Processing, vol. 142, 104193, October 2023.
DOIPDFCode
- M. López-Pérez, P. Morales-Álvarez, L. Cooper, C. Felicelli, J Goldstein, B. Vadasz , R. Molina, and A.K. Katsaggelos, “Learning from crowds for automated histopathological image segmentation”, Computerized Medical Imaging and Graphics, vol. 112, 102327, March 2024.
DOICode
- J. Pérez-Cano, Y. Wu, A. Schmidt, M. López-Pérez, P. Morales-Álvarez, R. Molina, and A. K. Katsaggelos, “An End-to-end Approach to combine Attention feature extraction and Gaussian Process models for Deep Multiple Instance Learning”, Expert Systems With Applications, vol. 240, 122296, April 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
- M. López-Pérez, A. Morquecho, A. Schmidt, F. Pérez-Bueno, A. Martín-Castro, J. Mateos, and R. Molina, “The CrowdGleason dataset: learning the Gleason grade from crowds and experts”, Computer Methods and Programs in Biomedicine, vol. 257, 108472, December 2024.
DOIPDF
Conference Papers
- P. Morales-Álvarez, W. Gong, A. Lamb, S. Woodhead, S. Peyton Jones, N. Pawlowski, M. Allamanis, and C. Zhang, “Simultaneous Missing Value Imputation and Structure Learning with Groups” in Neural Information Processing Systems (NeurIPS), December 2022.
DOIPDF
- M. López-Pérez, P. Morales-Álvarez, L. Cooper, R. Molina, and A.K. Katsaggelos, “Crowdsourcing segmentation of histopathological images using annotations provided by medical students” in Artificial Intelligence in Medicine (AIME), edited by Springer, 245-249, June 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.
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- 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.
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- 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.
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- 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.
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