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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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Miscellaneous |
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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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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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Miscellaneous |
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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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