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E Fernandez-Nofrerias, J. Mauri, A. Tovar, L. Cano, E. Martinez, C. Julia, et al. (2001). Correspondencia de las imagenes de angiografia y ecografia intracoronaria: La fusion..
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Debora Gil, Katerine Diaz, Carles Sanchez, & Aura Hernandez-Sabate. (2020). Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images.
Abstract: 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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David Rotger, Petia Radeva, & Oriol Rodriguez. (2006). Vessel Tortuosity Extraction from IVUS Images.
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David Rotger, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Internal and External Coronary Vessel Images Registration..
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David Rotger, Petia Radeva, E Fernandez-Nofrerias, & J. Mauri. (2002). Registering External and Internal Morphological Images of Coronary Vessels..
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David Rotger, Petia Radeva, E Fernandez-Nofrerias, & J. Mauri. (2002). Multimodal Registration of Intravascular Ultrasound Images and Angiography..
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David Rotger, Petia Radeva, Cristina Cañero, Juan J. Villanueva, J. Mauri, E Fernandez-Nofrerias, et al. (2001). Corresponding IVUS and Angiogram Image Data.
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David Rotger, Cristina Cañero, Petia Radeva, J. Mauri, E. Fernandez, A. Tovar, et al. (2001). 3D Interactive Visualization and Volumetric Measurements of Coronary Vessels in IVUS..
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David Rotger, Cristina Cañero, Petia Radeva, J. Mauri, E. Fernandez, A. Tovar, et al. (2001). Advanced Visualization of 3D data of Intravascular Ultrasound Images..
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David Rotger. (2002). Multimodal Registration of Intravascular Ultrasound Images and Angiography.
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David Pujol Perich, Albert Clapes, & Sergio Escalera. (2023). SADA: Semantic adversarial unsupervised domain adaptation for Temporal Action Localization.
Abstract: Temporal Action Localization (TAL) is a complex task that poses relevant challenges, particularly when attempting to generalize on new -- unseen -- domains in real-world applications. These scenarios, despite realistic, are often neglected in the literature, exposing these solutions to important performance degradation. In this work, we tackle this issue by introducing, for the first time, an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL, which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions, ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo, that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods, attaining a performance boost of up to 6.14% mAP.
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David Masip, M. Bressan, & Jordi Vitria. (2004). Classifier Combination Applied to Real Time Face Detection and Classification.
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David Masip, & Jordi Vitria. (2003). On the Nearest Neighbor Approach for Gender Recognition.
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David Masip, & Jordi Vitria. (2004). Real Time Face Detection and Verification for Uncontrolled Environments.
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David Masip, & Jordi Vitria. (2004). Object Recognition using Boosted Adaptive Features..
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