Uncertainty aware models for the diagnosis of primary and secondary skin tumours
Project PID2022-140189OB-C22: Modelos conscientes de la incertidumbre para el diagnóstico de tumores cutáneos primarios y secundarios
Part of the Coordinated Project Acercando la patología computacional a la práctica clínica: un sistema de IA para el diagnóstico de tumores cutáneos primarios y secundarios (ASSIST)
Journal Papers
- 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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- 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.
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- 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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- 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.
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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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- F. Pérez-Bueno, K. Engan, and R. Molina, “Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD”, Artificial Intelligence in Medicine, October 2024.
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- 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.
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Conference Papers
- F. M. Castro-Macías, F. Pérez-Bueno, M. Vega, J. Mateos, R. Molina, and A. K. Katsaggelos, “Blind Color Deconvolution and Classification of Histological Images Using the Hyperbolic Secant Prior” in IEEE International Symposium on Biomedical Imaging, May 2024.
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- F. M. Castro-Macías, F. Pérez-Bueno, M. Vega, J. Mateos, R. Molina, and A. K. Katsaggelos, “Bayesian Blind Image Deconvolution using an Hyperbolic-Secant prior” in 2024 IEEE International Conference on Image Processing (ICIP 2024), 1500-1506, October 2024.
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- F. M. Castro-Macías, P. Morales-Álvarez, Y. Wu, R. Molina, and A.K. Katsaggelos, “Sm: enhanced localization in Multiple Instance Learning for medical imaging classification” in 2024 Conference on Neural Information Processing Systems (NeurIPS 2024), December 2024. In press.
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- F. J. Sáez-Maldonado, J. Maroñas-Molano, and D. Hernández-Lobato, “Mode Collapse in Variational Deep Gaussian Processes” in NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty, December 2024. In press.
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Theses
- 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.
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