Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor. - Miguel López-Pérez; Adrián Colomer; María Á. Sales; Rafael Molina 001; Valery Naranjo (IDEAL (1), 2019)

A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. - Ángel E. Esteban; Miguel López-Pérez; Adrián Colomer; María Á. Sales; Rafael Molina 001; Valery Naranjo (Comput. Methods Programs Biomed., 2019)

A TV-based image processing framework for blind color deconvolution and classification of histological images. - Fernando Pérez-Bueno; Miguel López-Pérez; Miguel Vega; Javier Mateos; Valery Naranjo; Rafael Molina 001; Aggelos K. Katsaggelos (Digit. Signal Process., 2020)

A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes. - Miguel López-Pérez; Luz García 001; M. Carmen Benítez; Rafael Molina 001 (IEEE Trans. Geosci. Remote. Sens., 2021)

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection. - Miguel López-Pérez; Arne Schmidt 005; Yunan Wu; Rafael Molina 001; Aggelos K. Katsaggelos (Comput. Methods Programs Biomed., 2022)

Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students. - Miguel López-Pérez; Pablo Morales-Álvarez; Lee A. D. Cooper; Rafael Molina 001; Aggelos K. Katsaggelos (AIME, 2023)

Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification. - Miguel López-Pérez; Pablo Morales-Álvarez; Lee A. D. Cooper; Rafael Molina 001; Aggelos K. Katsaggelos (IEEE Access, 2023)

Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. - Rocío del Amor; Jose Pérez-Cano; Miguel López-Pérez; Liria Terradez; José Aneiros-Fernández; Sandra Morales; Javier Mateos; Rafael Molina 001; Valery Naranjo (Artif. Intell. Medicine, 2023)

Unsupervised Prediction of Blastocyst Development from Oocyte Images. - Natalia P. García-de-la-Puente; Elena Payá-Bosch; Lucía Murria; Miguel López-Pérez; Marcos Meseguer; Valery Naranjo (DSA ISC (2), 2024)

Domain Adaptation for Unsupervised Cancer Detection: An Application for Skin Whole Slides Images from an Interhospital Dataset. - Natalia P. García-de-la-Puente; Miguel López-Pérez; Laëtitia Launet; Valery Naranjo (MICCAI (4), 2024)

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images. - Neel Kanwal; Miguel López-Pérez; Umay Kiraz; Tahlita C. M. Zuiverloon; Rafael Molina 001; Kjersti Engan (Comput. Medical Imaging Graph., 2024)

Learning from crowds for automated histopathological image segmentation. - Miguel López-Pérez; Pablo Morales-Álvarez; Lee A. D. Cooper; Christopher Felicelli; Jeffery A. Goldstein; Brian Vadasz; Rafael Molina 001; Aggelos K. Katsaggelos (Comput. Medical Imaging Graph., 2024)

The CrowdGleason dataset: Learning the Gleason grade from crowds and experts. - Miguel López-Pérez; Alba Morquecho; Arne Schmidt 005; Fernando Pérez-Bueno; Aurelio Martín-Castro; Javier Mateos; Rafael Molina 001 (Comput. Methods Programs Biomed., 2024)

An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection. - Jose Pérez-Cano; Yunan Wu; Arne Schmidt 005; Miguel López-Pérez; Pablo Morales-Álvarez; Rafael Molina 001; Aggelos K. Katsaggelos (Expert Syst. Appl., 2024)

CR-AI4SkIN dataset. - Rocío del Amor; Jose Pérez-Cano; Miguel López-Pérez; Liria Terradez; José Aneiros-Fernández; Sandra Morales; Javier Mateos; Rafael Molina 001; Valery Naranjo (Zenodo, 2024)

The CrowdGleason dataset: learning the Gleason grade from crowds and experts. - Miguel López-Pérez; Alba Morquecho; Arne Schmidt 005; Fernando Pérez-Bueno; Aurelio Martín-Castro; Javier Mateos; Rafael Molina 001 (Zenodo, 2024)

Are Generative Models Fair? A Study of Racial Bias in Dermatological Image Generation. - Miguel López-Pérez; Søren Hauberg; Aasa Feragen (SCIA (2), 2025)

Bayesian generative models can flag performance loss, bias, and out-of-distribution image content. - Miguel López-Pérez; Marco Miani; Valery Naranjo; Søren Hauberg; Aasa Feragen (CoRR, 2025)