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Guillermo Torres, Jan Rodríguez Dueñas, Sonia Baeza, Antoni Rosell, Carles Sanchez, & Debora Gil. (2023). "Prediction of Malignancy in Lung Cancer using several strategies for the fusion of Multi-Channel Pyradiomics Images " In 7th Workshop on Digital Image Processing for Medical and Automotive Industry in the framework of SYNASC 2023.
Abstract: This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.
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Enric Marti. (2008)." Project Based Learning in engineering" . Lleida.
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Jaume Garcia. (2002)." Propagacio de fronts per a la segmentacio en imatges IVUS" .
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Hanne Kause, Patricia Marquez, Andrea Fuster, Aura Hernandez-Sabate, Luc Florack, Debora Gil, et al. (2015)." Quality Assessment of Optical Flow in Tagging MRI" In 5th Dutch Bio-Medical Engineering Conference BME2015.
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Carles Sanchez, Debora Gil, R. Tazi, Jorge Bernal, Y. Ruiz, L. Planas, et al. (2015)." Quasi-real time digital assessment of Central Airway Obstruction" In 3rd European congress for bronchology and interventional pulmonology ECBIP2015.
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Antoni Rosell, Sonia Baeza, S. Garcia-Reina, JL. Mate, Ignasi Guasch, I. Nogueira, et al. (2022)." Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results." European Respiratory Journal, 60(66).
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Ernest Valveny, & Enric Marti. (1999)." Recognition of lineal symbols in hand-written drawings using deformable template matching" In Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes.
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Aura Hernandez-Sabate, Jose Elias Yauri, Pau Folch, Miquel Angel Piera, & Debora Gil. (2022). "Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals " . Applied Sciences, 12(5), 2298.
Abstract: The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.
Keywords: Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion
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Enric Marti, Jordi Vitria, & Alberto Sanfeliu. (1998). "Reconocimiento de Formas y Análisis de Imágenes ". AERFAI.
Abstract: Los sistemas actuales de reconocimiento automático del lenguaje oral se basan en dos etapas básicas de procesado: la parametrización, que extrae la evolución temporal de los parámetros que caracterizan la voz, y el reconocimiento propiamente dicho, que identifica la cadena de palabras de la elocución recibida con ayuda de los modelos que representan el conocimiento adquirido en la etapa de aprendizaje. Tomando como línea divisoria la palabra, dichos modelos son de tipo acústicofonético o gramatical. Los primeros caracterizan las palabras incluidas en el vocabulario de la aplicación o tarea a la que está orientado el sistema de reconocimiento, usando a menudo para ello modelos de unidades de habla de extensión inferior a la palabra, es decir, de unidades subléxicas. Por otro lado, la gramática incluye el conocimiento acerca de las combinaciones permitidas de palabras para formar las frases o su probabilidad. Queda fuera del esquema la denominada comprensión del habla, que utiliza adicionalmente el conocimiento semántico y pragmático para captar el significado de la elocución de entrada al sistema a partir de la cadena (o cadenas alternativas) de palabras que suministra el reconocedor.
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M.Gomez, Josefina Mauri, Eduard Fernandez-Nofrerias, Oriol Rodriguez-Leon, Carme Julia, Debora Gil, et al. (2002)." Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria" In XXXVIII Congreso Nacional de la Sociedad Española de Cardiología..
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