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Enric Marti; J. Rocarias; A. Sanchez; Petia Radeva; Ricardo Toledo; Jordi Vitria |
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Caronte: un gestor documental para asignaturas del EEES |
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2006 |
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III Jornades de Innovacio Docent |
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UAB, Bellaterra |
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IAM;RV;OR;MILAB;ADAS;MV |
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BCNPCL @ bcnpcl @ MRS2006b |
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1124 |
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Enric Marti; Debora Gil; Carme Julia |
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Una experiència en PBL per a la docència de Gràfics per Computador |
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2005 |
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II Jornades d’innovació Docent |
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Aprenentatge Basat en Projectes; Aprenentatge Basat en Problemes; Problem Based Learning; ECTS; EEES; Computer Graphics; OpenGL. |
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En aquest article es presenta una experiència en ABP feta el curs 2004-05 en Gràfics per Computador 2, assignatura optativa de 3er curs d’Enginyeria Informàtica impartida a l’ETSE. En l’article s’explica l’organització docent abans d’ABP, basada en classes magistrals. Després es mostra l’organització en ABP i es quantifica en ECTS l’esforç de l’alumne en ambdues organitzacions. Essent conscient del diferent interès de l’alumnat per l’assignatura, se’ls hi ofereix dos itineraris: el de classes magistrals i d’ABP. Es mostren alguns resultats dels alumnes d’ABP i també les primeres enquestes realitzades als alumnes. S’exposen les conclusions en el primer any de l’experiència, plantejant temes de discussió. S’ha procurat que la proposta no desbordi l’esforç del professorat. Per això s’ofereix el doble itinerari, per a canalitzar per ABP els alumnes més interessats i permetre a la resta que realitzin el curs amb l’organització clàsica de l’assignatura: classes magistrals de teoria, problemes i pràctiques. |
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IAM;ADAS; |
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IAM @ iam @ MGJ2005c |
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1594 |
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Elena Valderrama; Joan Oliver; Josep Maria-Basart; Enric Marti; Petia Radeva; Ricardo Toledo; R.Vilanova;F.Ced; J.Muñoz; S.Vacchina |
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Convergencia al EEES de la ingeniería informática. Título de Grado en tecnología (Informática) |
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2005 |
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I Jornades de Innovació Docent |
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Elena Valderrama |
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IAM;RV;MILAB;ADAS |
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IAM @ iam @ VOB2005 |
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1652 |
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Enric Marti; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate; Jaume Garcia; Carme Julia; Marc Vivet |
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Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Gráficos por Computador |
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2009 |
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I Congreso de Docencia Universitaria |
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Aprendizaje Basado en Proyectos; Project Based Learning; Aprendizaje Cooperativo; Recursos Virtuales para el Aprendizaje Cooperativo; Moodle |
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Presentamos una experiencia en Aprendizaje Basado en Proyectos (ABP) realizada los últimos cuatro años en Gráficos por Computador 2, asignatura de Ingeniería Informática, de la Escuela Técnica Superior de Ingeniería (ETSE) de la Universidad Autónoma de Barcelona (UAB). Utilizamos un entorno Moodle adaptado por nosotros llamado Caronte para poder gestionar la documentación generada en ABP. Primero se presenta la asignatura, basada en dos itinerarios para cursarla: ABP y TPPE (Teoría, Problemas, Prácticas, Examen). El alumno debe escoger uno de ellos. Ambos itinerarios generan una cantidad importante de documentación (entregas de trabajos y prácticas, correcciones, ejercicios, etc.) a gestionar. En la comunicación presentamos los espacios electrónicos Moodle de ambos itinerarios. Finalmente, mostramos los resultados de encuestas realizadas a los alumnos para finalmente exponer las conclusiones de la experiencia en ABP y el uso de Moodle, así como plantear mejoras y temas de discusión. |
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Vigo (Spain) |
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IAM;ADAS; |
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IAM @ iam @ MRG2009a |
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1602 |
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Albert Andaluz; Francesc Carreras; Debora Gil; Jaume Garcia |
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Una aplicació amigable pel càlcul de indicadors clínics del ventricle esquerre |
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2010 |
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Forum Biocat 2010 |
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Lonja de Mar,Barcelona (Spain) |
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CVC |
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Biocat |
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Barcelona |
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Catalan |
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IAM @ iam @ ACG2010 |
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1483 |
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Enric Marti; Debora Gil; Carme Julia |
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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 |
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2008 |
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Experiències docents innovadores de la UAB en ciències experimentals i tecnologies i en ciències de la salud |
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1 |
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57-68 |
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UAB |
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IDES-UAB; M.Enric Martinez, E.A. |
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978-84-490-2576-1 |
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IAM;ADAS; |
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IAM @ iam @ MGJ2008 |
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1592 |
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Enric Marti; J. Rocarias; Petia Radeva; H. Tizon; Jordi Vitria |
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Caronte. Un gestor documental para asignaturas de universidad en el EEES |
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2007 |
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Desarrollo de gestion de grupos, encuestas y autoevaluacion, MoodleMoot 2007 |
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IAM;OR;MILAB;MV |
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BCNPCL @ bcnpcl @ MRR2007b |
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1128 |
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Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
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Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images |
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2023 |
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Arxiv |
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107241 |
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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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IAM |
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Admin @ si @ CCG2023 |
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3855 |
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Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua |
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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge |
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2018 |
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Arxiv |
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BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST |
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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. |
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ADAS; 600.118;MILAB;IAM |
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Admin @ si @ BRJ2018 |
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3252 |
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Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate |
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Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images |
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2020 |
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Arxiv |
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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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IAM; 600.139; 600.145; 601.337 |
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Admin @ si @ GDS2020 |
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3474 |
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