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Enric Marti; Debora Gil; Marc Vivet ; Carme Julia |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática |
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2008 |
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Actas V Jornadas Internacionales de Innovación Universitaria |
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Aprendizaje cooperativo; aprendizaje basado en proyectos; experiencias docentes. |
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En este trabajo se presentan los resultados de la aplicación de la metodología del aprendizaje cooperativo a la docencia de dos asignaturas de programación en ingeniería informática. ‘Algoritmos y programación’ y ‘Lenguajes de programación’ son dos asignaturas complementarias que se organizan entorno a un proyecto común que engloba los contenidos de ambas asignaturas. En la docencia de una parte muy importante de estas asignaturas, la metodología del aprendizaje cooperativo se ha adaptado a sus características específicas. Como muestra de esta adaptación presentamos dos ejemplos de las actividades desarrolladas dentro de la docencia de estas asignaturas. Después de tres años de aplicación, el análisis a nivel cualitativo y cuantitativo de los resultados muestra que éstos son muy satisfactorios y que la aplicación del método cooperativo ha mejorado de forma considerable el rendimiento de los alumnos en ambas asignaturas. |
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IAM @ iam @ MGV2008a |
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1598 |
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Author |
Enric Marti; Debora Gil; Marc Vivet; Carme Julia |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
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Title |
Balance de cuatro años de experiencia en la implantación de la metodología de Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en ingeniería Informática |
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2008 |
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VIII Jornadas de Innovación Universitaria |
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IAM @ iam @ MGV2008b |
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1599 |
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Author |
Enric Marti; Debora Gil; Marc Vivet; Carme Julia |
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Title |
Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Gráficos por Computador |
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2008 |
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VIII Jornada sobre Aprendizaje Cooperativo, I Jornada sobre innovacion Docente |
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IAM;ADAS; |
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IAM @ iam @ MRG2008b |
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1600 |
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Enric Marti; Jaume Rocarias; Debora Gil; Aura Hernandez-Sabate; Jaume Garcia; Carme Julia; Marc Vivet |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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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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IAM;ADAS; |
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IAM @ iam @ MRG2009a |
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1602 |
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Author |
Enric Marti; Carme Julia; Debora Gil |
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Title |
PBL en la docencia de gráficos por computador |
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2007 |
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VII Jornadas de Aprendizaje Cooperativo (JAC07) |
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1 |
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53-62 |
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IAM @ iam @ MJG2007b |
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1604 |
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Enric Marti; Ferran Poveda; Antoni Gurgui; Jaume Rocarias; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Una propuesta de seguimiento, tutorías on line y evaluación en la metodología de Aprendizaje Basado en Proyectos |
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2013 |
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IV Congreso Internacional UNIVEST |
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Poster |
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Girona |
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Admin @ si @ MPG2013a |
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2304 |
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Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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IAM; ADAS; 600.076; 600.063; 600.075 |
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Admin @ si @ MGG2014 |
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2457 |
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Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
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Title |
Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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Admin @ si @ SRM2014 |
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Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen |
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Title |
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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Admin @ si @ CCG2023 |
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3855 |
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Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate |
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Title |
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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