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Fernando Vilariño, & Enric Marti. (2008)." New didactic techniques in the EHES applying mobile technologies" .
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Enric Marti, Ferran Poveda, Antoni Gurgui, Jaume Rocarias, & Debora Gil. (2013). "Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos ".
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Enric Marti, Antoni Gurgui, Debora Gil, Aura Hernandez-Sabate, Jaume Rocarias, & Ferran Poveda. (2014). "ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos ".
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Carles Sanchez, Oriol Ramos Terrades, Patricia Marquez, Enric Marti, Jaume Rocarias, & Debora Gil. (2014). "Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías ".
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Pau Cano, Alvaro Caravaca, Debora Gil, & Eva Musulen. (2023). "Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images ".
Abstract: This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori.
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Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, et al. (2018)." Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge" .
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
Keywords: BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST
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Debora Gil, Katerine Diaz, Carles Sanchez, & Aura Hernandez-Sabate. (2020). "Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images ".
Abstract: Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.
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Oriol Ramos Terrades, Albert Berenguel, & Debora Gil. (2020). "A flexible outlier detector based on a topology given by graph communities ".
Abstract: Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.
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Enric Marti, Debora Gil, & Carme Julia. (2008)." Experiencia d aplicació de la metodología d aprenentatge per proyectes en assignatures d Enginyeria Informàtica per a una millor adaptació als crèdits ECTS i EEES" (IDES-UAB, & E. A. M.Enric Martinez, Eds.) (Vol. 1). UAB.
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Enric Marti, Jaume Rocarias, Debora Gil, Marc Vivet, & Carme Julia. (2008). "Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Graficos por Computador ".
Abstract: En esta comunicación presentamos una experiencia en Aprendizaje Basado en Proyectos (Project
Based Learning – PBL) realizada los últimos cuatro años (cursos del 2004-05 al 2007-08) en Gráficos
por Computador 2, asignatura optativa de tercer curso de Ingeniería Informática, titulación impartida
en la Escuela Técnica Superior de Ingeniería (ETSE) de la Universidad Autónoma de Barcelona
(UAB).
Fruto de la constante voluntad de mejora de la organización ABP de nuestra asignatura nos decidimos
a utilizar una herramienta LMS (Learning Management System) basada en Moodle y adaptada por
nosotros llamada Caronte para poder gestionar la documentación generada en ABP, y añadir una
componente semipresencial a la asignatura.
En primer lugar se presenta la organización de nuestra asignatura, basada proponer al alumno dos
itinerarios para cursarla: el itinerario ABP y el itinerario basado en clases magistrales i examen que
llamaremos TPPE (Teoría, Problemas, Prácticas, Examen). La dinámica ABP nos genera una cantidad
importante de documentación entre los grupos y el profesor, aparte de el feedback que el profesor
genera a los alumnos.
En la segunda parte del artículo presentamos los espacios docentes electrónicos de ambos itinerarios,
con los que trabajan los alumnos.
Finalmente, mostramos los resultados obtenidos de alumnos matriculados y de encuestas de valoración
realizados por los alumnos para finalmente exponer las conclusiones de estos cuatro años de
experiencia en ABP y en el uso de recursos virtuales en ABP, así como plantear mejoras y temas de
discusión sobre ABP.
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