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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

2021

LNDb Challenge on automatic lung cancer patient management

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Atwal, G; Phoulady, HA; Chen, XY; Chen, RZ; Li, JL; Wang, LS; Galdran, A; Bouchachia, H; Kaluva, KC; Vaidhya, K; Chunduru, A; Tarai, S; Nadimpalli, SPP; Vaidya, S; Kim, I; Rassadin, A; Tian, ZH; Sun, ZW; Jia, YZ; Men, XJ; Ramos, I; Cunha, A; Campilho, A;

Publication
MEDICAL IMAGE ANALYSIS

Abstract

2020

O-MedAL: Online active deep learning for medical image analysis

Authors
Smailagic, A; Costa, P; Gaudio, A; Khandelwal, K; Mirshekari, M; Fagert, J; Walawalkar, D; Xu, SS; Galdran, A; Zhang, P; Campilho, A; Noh, HY;

Publication
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under:. Technologies > Machine Learning. Technologies > Classification. Application Areas > Health Care. © 2020 Wiley Periodicals, Inc.

2020

A fast image dehazing method that does not introduce color artifacts

Authors
Vazquez Corral, J; Galdran, A; Cyriac, P; Bertalmio, M;

Publication
JOURNAL OF REAL-TIME IMAGE PROCESSING

Abstract
We propose a method for color dehazing with four main characteristics: it does not introduce color artifacts, it does not depend on inverting any physical equation, it is based on models of visual perception, and it is fast, potentially real time. Our method converts the original input image to the HSV color space and works in the saturation and value domains by: (1) reducing the value component via a global constrained histogram flattening; (2) modifying the saturation component in consistency with the previous reduced value; and (3) performing a local contrast enhancement in the value component. Results show that our method competes with the state-of-the-art when dealing with standard hazy images, and outperforms it when dealing with challenging haze cases. Furthermore, our method is able to dehaze a FullHD image on a GPU in 90 ms. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

2019

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Authors
Al Hajj, H; Lamard, M; Conze, PH; Roychowdhury, S; Hu, XW; Marsalkaite, G; Zisimopoulos, O; Dedmari, MA; Zhao, FQ; Prellberg, J; Sahu, M; Galdran, A; Araujo, T; Vo, DM; Panda, C; Dahiya, N; Kondo, S; Bian, ZB; Vandat, A; Bialopetravicius, J; Flouty, E; Qiu, CH; Dill, S; Mukhopadhyay, A; Costa, P; Aresta, G; Ramamurthys, S; Lee, SW; Campilho, A; Zachow, S; Xia, SR; Conjeti, S; Stoyanov, D; Armaitis, J; Heng, PA; Macready, WG; Cochener, B; Quellec, G;

Publication
MEDICAL IMAGE ANALYSIS

Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. © 2018 Elsevier B.V.

2019

EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection

Authors
Costa, P; Araujo, T; Aresta, G; Galdran, A; Mendonca, AM; Smailagic, A; Campilho, A;

Publication
PROCEEDINGS OF MVA 2019 16TH INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA)

Abstract