Publicaciones en colaboración con investigadores/as de Northwestern University (12)

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. Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection

    Artificial Intelligence, Vol. 331

  3. Learning from crowds for automated histopathological image segmentation

    Computerized Medical Imaging and Graphics, Vol. 112

2023

  1. 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)

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

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

  3. Probabilistic fusion of crowds and experts for the search of gravitational waves

    Knowledge-Based Systems, Vol. 261

  4. Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection

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

2022

  1. Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO

    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, Núm. 3, pp. 1534-1551

2021

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

    Scientific Reports, Vol. 11, Núm. 1

2019

  1. Learning from crowds with variational Gaussian processes

    Pattern Recognition, Vol. 88, pp. 298-311

  2. Scalable and efficient learning from crowds with Gaussian processes

    Information Fusion, Vol. 52, pp. 110-127

2018

  1. Passive millimeter wave image classification with large scale Gaussian processes

    Proceedings - International Conference on Image Processing, ICIP