Miguel López-Pérez

Orcid: 0000-0003-2965-0624

According to our database1, Miguel López-Pérez authored at least 13 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

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 Syst. Appl., 2024

The CrowdGleason dataset: Learning the Gleason grade from crowds and experts.
Comput. Methods Programs Biomed., 2024

Learning from crowds for automated histopathological image segmentation.
Comput. Medical Imaging Graph., 2024

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images.
Comput. Medical Imaging Graph., 2024

Domain Adaptation for Unsupervised Cancer Detection: An Application for Skin Whole Slides Images from an Interhospital Dataset.
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.
Artif. Intell. Medicine, November, 2023

Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification.
IEEE Access, 2023

Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students.
Proceedings of the Artificial Intelligence in Medicine, 2023

2022
Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.
Comput. Methods Programs Biomed., 2022

2021
A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes.
IEEE Trans. Geosci. Remote. Sens., 2021

2020
A TV-based image processing framework for blind color deconvolution and classification of histological images.
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.
Comput. Methods Programs Biomed., 2019

Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2019, 2019


  Loading...