Publicacións nas que colabora con MIGUEL LÓPEZ PÉREZ (14)

2024

  1. 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, Vol. 240

  2. Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images

    Computerized Medical Imaging and Graphics, Vol. 112

  3. CR-AI4SkIN dataset

    Zenodo

  4. CR-AI4SkIN dataset

    Zenodo

  5. Learning from crowds for automated histopathological image segmentation

    Computerized Medical Imaging and Graphics, Vol. 112

2023

  1. Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset

    Artificial Intelligence in Medicine, Vol. 145

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

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification

    IEEE Access, Vol. 11, pp. 6922-6934

2022

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

    Computer Methods and Programs in Biomedicine, Vol. 219

2021

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

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Núm. 5, pp. 3875-3890

  2. Learning from crowds in digital pathology using scalable variational Gaussian processes

    Scientific Reports, Vol. 11, Núm. 1

2020

  1. A TV-based image processing framework for blind color deconvolution and classification of histological images

    Digital Signal Processing: A Review Journal, Vol. 101

2019

  1. 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, Vol. 178, pp. 303-317

  2. Classifying prostate histological images using deep Gaussian processes on a new optical density granulometry-based descriptor

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)