Journal Papers
- 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
- 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.
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- 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.
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- 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.
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- S. Yang, F. Pérez-Bueno, F. M. Castro-Macías, and R. Molina, and A. K. Katsaggelos, “BCD-net: Stain separation of histological images using deep variational Bayesian blind color deconvolution”, Digital Signal Processing Journal, vol. 145, February 2024.
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- N. Kanwal, M. López-Pérez, U. Kiraz, T. Zuiverloon, R. Molina, and K. Engan, “Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images”, Computerized Medical Imaging and Graphics, March 2024.
DOIPDF
- 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.
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- 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.
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Conference Papers
- 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, June 2023.
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
- 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.
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- 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.
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- 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.
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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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