Gabriel García
Orcid: 0000-0001-9900-886XAffiliations:
- Technical University of Valencia, Institute of Research and Innovation in Bioengineering, Spain
According to our database1,
Gabriel García
authored at least 13 papers
between 2018 and 2022.
Collaborative distances:
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Bibliography
2022
Proceedings of the 30th European Signal Processing Conference, 2022
2021
Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging.
Sensors, 2021
Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies.
Comput. Methods Programs Biomed., 2021
A novel self-learning framework for bladder cancer grading using histopathological images.
Comput. Biol. Medicine, 2021
Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks.
Artif. Intell. Medicine, 2021
Proceedings of the 29th European Signal Processing Conference, 2021
2020
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks.
CoRR, 2020
Prostate Gland Segmentation in Histology Images via Residual and Multi-resolution U-NET.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2020, 2020
Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circumpapillary OCT Images.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2020, 2020
Glaucoma Detection From Raw Circumpapillary OCT Images Using Fully Convolutional Neural Networks.
Proceedings of the IEEE International Conference on Image Processing, 2020
2019
First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning.
Entropy, 2019
Computer Aid-System to Identify the First Stage of Prostate Cancer Through Deep-Learning Techniques.
Proceedings of the 27th European Signal Processing Conference, 2019
2018
Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018