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. [BibTeX entry][Abstract][ (1526 KB.)][doi: 10.1016/j.dsp.2021.103285]
- 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. [BibTeX entry][ (9773 KB.)][doi: https://doi.org/10.1016/j.cmpb.2021.106453]
- 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. [BibTeX entry][Abstract][ (32253 KB.)][doi: https://doi.org/10.1016/j.compmedimag.2022.102048]
- 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, 2022. [BibTeX entry] (Submitted).
- 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, May 2022. [BibTeX entry] (Submitted).
- 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. [BibTeX entry][ (2346 KB.)][doi: https://doi.org/10.1016/j.cmpb.2022.106783]
- 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. [BibTeX entry][ (2346 KB.)][doi: https://doi.org/10.1016/j.cmpb.2022.106783]
- 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. [BibTeX entry][Abstract][ (1149 KB.)][doi: 10.1016/j.knosys.2022.110183]
- 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, June 2023. [BibTeX entry] (Submitted).
- 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, 2022. [BibTeX entry] (Accepted for publication).
- 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, 2023. [BibTeX entry] (Accepted for publication).
- 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, 2023. [BibTeX entry] (Submitted).
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), New Orleans, Louisiana, USA, December 2022. [BibTeX entry]
- F. Pérez-Bueno, K. Engan, and R. Molina, “A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H&E Histological Images” in Artificial Intelligence in Medicine (AIME). Highlighted as one of the top-3 best papers of the conference., edited by Springer, vol. 13897, 207-217, Portoroz (Eslovenia), June 2023. [BibTeX entry][Abstract][ (8649 KB.)]
- 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, Portoroz (Slovenia), June 2023. [BibTeX entry][Abstract]
- A. Schmidt, P. Morales-Álvarez, and R. Molina, “Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation”, The International Conference on Computer Vision (ICCV), 2023.
Theses
- 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. [BibTeX entry][ (12485 KB.)]
- 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. [BibTeX entry][Abstract][ (115453 KB.)]