Gabriel García

Orcid: 0000-0001-9900-886X

Affiliations:
  • 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:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2022
Glaucoma Grading VIA Mean Defect Back Propagation From OCT Images.
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

A Self-Training Framework for Glaucoma Grading In OCT B-Scans.
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


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