RAFAEL
MOLINA SORIANO
CATEDRÁTICO DE UNIVERSIDAD
MIGUEL
LÓPEZ PÉREZ
Investigador en el periodo 2023-2023
Publicaciones en las que colabora con MIGUEL LÓPEZ PÉREZ (14)
2024
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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
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Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images
Computerized Medical Imaging and Graphics, Vol. 112
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CR-AI4SkIN dataset
Zenodo
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CR-AI4SkIN dataset
Zenodo
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Learning from crowds for automated histopathological image segmentation
Computerized Medical Imaging and Graphics, Vol. 112
2023
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Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset
Artificial Intelligence in Medicine, Vol. 145
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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)
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Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification
IEEE Access, Vol. 11, pp. 6922-6934
2022
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Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection
Computer Methods and Programs in Biomedicine, Vol. 219
2021
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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
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Learning from crowds in digital pathology using scalable variational Gaussian processes
Scientific Reports, Vol. 11, Núm. 1
2020
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A TV-based image processing framework for blind color deconvolution and classification of histological images
Digital Signal Processing: A Review Journal, Vol. 101
2019
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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
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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)