Publications

Jose Pérez-Cano, Yunan Wu, Arne schmidt, Miguel López-Pérez, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos (2024). An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection. Expert Systems with Applications, April 2024, pp. 122296.

Cite DOI

Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Christopher Felicelli, Jeffery Goldstein, Brian Vadasz, Rafael Molina, Aggelos K. Katsaggelos (2024). Learning from crowds for automated histopathological image segmentation. Computerized Medical Imaging and Graphics, March 2024, pp. 102327.

Cite Code Dataset DOI

Neel Kanwal, Miguel López-Pérez, Umay Kiraz, Tahlita C.M. Zuiverloon, Rafael Molina, Kjersti Engan (2024). Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images. Computerized Medical Imaging and Graphics, March 2024, pp. 102321.

Cite DOI

Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos (2023). Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students. Artificial Intelligence in Medicine (AIME), edited by Springer, Portoroz (Slovenia), June 2023, pp. 245-249.

Cite Code Dataset DOI

Jose Pérez-Cano, Rocío del Amor, Miguel López-Pérez, Liria Terradez, Jose Aneiros-Fernández, Sandra Morales, Javier Mateos, Rafael Molina, Valery Naranjo (2023). Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. Artificial Intelligence in Medicine (AIM), Volume 145, November 2023, pp. 102686.

Cite DOI

Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos (2023). Deep Gaussian Processes for classification with multiple noisy annotators. Application to breast cancer tissue classification. IEEE Access, Volume 11, January 2023, pp. 6922 - 6934.

Cite Code DOI

Miguel López-Pérez, Arne schmidt, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos (2022). Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection. Computer Methods and Programs in Biomedicine, Volume 219, June 2022, 106783.

Cite Code DOI

Miguel López-Pérez, Mohamed Amgad, Pablo Morales-Álvarez, Pablo Ruiz, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos (2021). Learning from crowds in digital pathology using scalable variational Gaussian processes. Scientific Reports, Volume 11, Number 1, June 2021, pp. 1-9.

Cite Code Dataset DOI

Miguel López-Pérez, Luz García, Carmen Benítez, Rafael Molina (2021). A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes. IEEE Transactions on Geoscience and Remote Sensing. Volume 59, Number 5, May 2021, pp. 3875-3890..

Cite DOI

Fernando Pérez-Bueno, Miguel López-Pérez, Miguel Vega, Javier Mateos, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos (2020). A TV-based image processing framework for blind color deconvolution and classification of histological images. Digital Signal Processing. Volume 101, no. 6, June 2020, 102727.

Cite DOI

Miguel López-Pérez, Adrián Colomer, María A. Sales, Valery Naranjo, Rafael Molina (2019). Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor. In IDEAL 2019.

Cite Dataset Project DOI

Ángel E. Esteban, Miguel López-Pérez, Adrián Colomer, María A. Sales, Rafael Molina, Valery Naranjo (2019). A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Computer Methods and Programs in Biomedicine. Volume 178, September 2019, pp. 303-317.

Cite Dataset Project DOI