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About

I received my degree in Mathematics at the University of Valencia in 2008. For the next course I was awarded with a grant from the  Fundación La Caixa" to do the M.S. "Mathematics Investigation", at the University of Valencia, together with the Polytechnical  University of Valencia. I developed the master project in the field of Computer Aided Design, in the topic of Pythagorean Hodograph Curves, under the supervision of Juan Monterde.
On October 2009, I joined the PDE line at the Basque Center for Applied Mathematics, to work mainly on the numerical treatment of PDEs. On October 2010, I obtained a fellowship from the "Fundación de Centros Tecnológicos - Iñaki Goenaga" (FCT-IG) to develop a PhD on Mathematical Image Processing, at the technological center Tecnalia Research and Innovation, under the supervision of David Pardo, from the UPV-EHU, together with Artzai Picón, from Tecnalia. On October 2010 I finished with honors the M.S. in "Mathematical Modelization, Statistics and Computation" at the UPV-EHU. On December 2015 I finally deffended my PhD Thesis on Image Restoration under Attenuating Media. From then to September 2016 I worked as a senior researcher in Tecnalia, and starting from September 2016, I am a Post-Doctoral fellow at INESC-TEC Porto, within the C-BER group under the supervision of Professor Aurélio Campilho.

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Publications

2018

Retinal image quality assessment by mean-subtracted contrast-normalized coefficients

Authors
Galdran, A; Araujo, T; Mendonca, AM; Campilho, A;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
The automatic assessment of visual quality on images of the eye fundus is an important task in retinal image analysis. A novel quality assessment technique is proposed in this paper. We propose to compute Mean-Subtracted Contrast-Normalized (MSCN) coefficients on local spatial neighborhoods of a given image and analyze their distribution. It is known that for natural images, such distribution behaves normally, while distortions of different kinds perturb this regularity. The combination of MSCN coefficients with a simple measure of local contrast allows us to design a simple but effective retinal image quality assessment algorithm that successfully discriminates between good and low-quality images, while delivering a meaningful quality score. The proposed technique is validated on a recent database of quality-labeled retinal images, obtaining results aligned with state-of-the-art approaches at a low computational cost. © 2018, Springer International Publishing AG.

2018

A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images

Authors
Costa, P; Galdran, A; Smailagic, A; Campilho, A;

Publication
IEEE ACCESS

Abstract
Diabetic retinopathy (DR) detection is a critical retinal image analysis task in the context of early blindness prevention. Unfortunately, in order to train a model to accurately detect DR based on the presence of different retinal lesions, typically a dataset with medical expert's annotations at the pixel level is needed. In this paper, a new methodology based on the multiple instance learning (MIL) framework is developed in order to overcome this necessity by leveraging the implicit information present on annotations made at the image level. Contrary to previous MIL-based DR detection systems, the main contribution of the proposed technique is the joint optimization of the instance encoding and the image classification stages. In this way, more useful mid-level representations of pathological images can be obtained. The explainability of the model decisions is further enhanced by means of a new loss function enforcing appropriate instance and mid-level representations. The proposed technique achieves comparable or better results than other recently proposed methods, with 90% area under the receiver operating characteristic curve (AUC) on Messidor, 93% AUC on DR1, and 96% AUC on DR2, while improving the interpretability of the produced decisions.

2018

Deep Convolutional Artery/Vein Classification of Retinal Vessels

Authors
Meyer, MI; Galdran, A; Costa, P; Mendonça, AM; Campilho, A;

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

Abstract
The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of 96 % on large caliber vessels, and an overall accuracy of 84 %. © 2018, Springer International Publishing AG, part of Springer Nature.

2018

Weakly Supervised Fog Detection

Authors
Galdran, A; Costa, P; Vazquez Corral, J; Campilho, A;

Publication
2018 IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece, October 7-10, 2018

Abstract

2018

A Pixel-Wise Distance Regression Approach for Joint Retinal Optical Disc and Fovea Detection

Authors
Meyer, MI; Galdran, A; Mendonça, AM; Campilho, A;

Publication
Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II

Abstract