Miguel López-Pérez

According to our database, Miguel López-Pérez authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, Miguel López-Pérez, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos

Expert Syst. Appl., 2024

Learning from crowds for automated histopathological image segmentation.

Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Christopher Felicelli, Jeffery Goldstein, Brian Vadasz, Rafael Molina, Aggelos K. Katsaggelos

Comput. Medical Imaging Graph., 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, Kjersti Engan

Comput. Medical Imaging Graph., 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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

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, Valery Naranjo

Artif. Intell. Medicine, November, 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, Aggelos K. Katsaggelos

IEEE Access, 2023

Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students.

Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos

Proceedings of the Artificial Intelligence in Medicine, 2023

2022

Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Miguel López-Pérez, Arne Schmidt, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos

Comput. Methods Programs Biomed., 2022

2021

A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes.

Miguel López-Pérez, Luz García, M. Carmen Benítez, Rafael Molina

IEEE Trans. Geosci. Remote. Sens., 2021

2020

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, Aggelos K. Katsaggelos

Digit. Signal Process., 2020

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, Valery Naranjo

Comput. Methods Programs Biomed., 2019

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, Valery Naranjo

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019