Search Results for author: Valery Naranjo

Found 13 papers, 5 papers with code

A self-training framework for glaucoma grading in OCT B-scans

no code implementations23 Nov 2021 Gabriel García, Adrián Colomer, Rafael Verdú-Monedero, José Dolz, Valery Naranjo

Particularly, the proposed two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain, which is then used to train the final target model.

Looking at the whole picture: constrained unsupervised anomaly segmentation

1 code implementation1 Sep 2021 Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz

In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility.

Lesion Segmentation

Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks

no code implementations25 Jun 2021 Gabriel García, Rocío del Amor, Adrián Colomer, Rafael Verdú-Monedero, Juan Morales-Sánchez, Valery Naranjo

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection.

Few-Shot Learning

Self-learning for weakly supervised Gleason grading of local patterns

1 code implementation21 May 2021 Julio Silva-Rodríguez, Adrián Colomer, Jose Dolz, Valery Naranjo

Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (k) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task.

whole slide images

WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images

1 code implementation21 May 2021 Julio Silva-Rodríguez, Adrián Colomer, Valery Naranjo

Regarding the estimation of the core-level Gleason score, we obtained a k of 0. 76 and 0. 67 between the model and two different pathologists.

Semantic Segmentation

Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection

1 code implementation21 May 2021 Julio Silva-Rodríguez, Adrián Colomer, María A. Sales, Rafael Molina, Valery Naranjo

The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies.

whole slide images

Analysis of Hand-Crafted and Automatic-Learned Features for Glaucoma Detection Through Raw Circmpapillary OCT Images

no code implementations9 Sep 2020 Gabriel García, Adrián Colomer, Valery Naranjo

Taking into account that glaucoma is the leading cause of blindness worldwide, we propose in this paper three different learning methodologies for glaucoma detection in order to elucidate that traditional machine-learning techniques could outperform deep-learning algorithms, especially when the image data set is small.

Cannot find the paper you are looking for? You can Submit a new open access paper.