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Victor Ponce, Mario Gorga, Xavier Baro, Petia Radeva, & Sergio Escalera. (2011). Análisis de la expresión oral y gestual en proyectos fin de carrera vía un sistema de visión artificial. ReVisión, 4(1).
Abstract: La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
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Frederic Sampedro, & Sergio Escalera. (2015). Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation. IETCV - IET Computer Vision, 9(3), 439–446.
Abstract: In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
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Frederic Sampedro, Anna Domenech, Sergio Escalera, & Ignasi Carrio. (2015). Deriving global quantitative tumor response parameters from 18F-FDG PET-CT scans in patients with non-Hodgkins lymphoma. NMC - Nuclear Medicine Communications, 36(4), 328–333.
Abstract: OBJECTIVES:
The aim of the study was to address the need for quantifying the global cancer time evolution magnitude from a pair of time-consecutive positron emission tomography-computed tomography (PET-CT) scans. In particular, we focus on the computation of indicators using image-processing techniques that seek to model non-Hodgkin's lymphoma (NHL) progression or response severity.
MATERIALS AND METHODS:
A total of 89 pairs of time-consecutive PET-CT scans from NHL patients were stored in a nuclear medicine station for subsequent analysis. These were classified by a consensus of nuclear medicine physicians into progressions, partial responses, mixed responses, complete responses, and relapses. The cases of each group were ordered by magnitude following visual analysis. Thereafter, a set of quantitative indicators designed to model the cancer evolution magnitude within each group were computed using semiautomatic and automatic image-processing techniques. Performance evaluation of the proposed indicators was measured by a correlation analysis with the expert-based visual analysis.
RESULTS:
The set of proposed indicators achieved Pearson's correlation results in each group with respect to the expert-based visual analysis: 80.2% in progressions, 77.1% in partial response, 68.3% in mixed response, 88.5% in complete response, and 100% in relapse. In the progression and mixed response groups, the proposed indicators outperformed the common indicators used in clinical practice [changes in metabolic tumor volume, mean, maximum, peak standardized uptake value (SUV mean, SUV max, SUV peak), and total lesion glycolysis] by more than 40%.
CONCLUSION:
Computing global indicators of NHL response using PET-CT imaging techniques offers a strong correlation with the associated expert-based visual analysis, motivating the future incorporation of such quantitative and highly observer-independent indicators in oncological decision making or treatment response evaluation scenarios.
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Adriana Romero, Petia Radeva, & Carlo Gatta. (2015). Meta-parameter free unsupervised sparse feature learning. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1716–1722.
Abstract: We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well.
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Tadashi Araki, Nobutaka Ikeda, Nilanjan Dey, Sayan Chakraborty, Luca Saba, Dinesh Kumar, et al. (2015). A comparative approach of four different image registration techniques for quantitative assessment of coronary artery calcium lesions using intravascular ultrasound. CMPB - Computer Methods and Programs in Biomedicine, 118(2), 158–172.
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