Browsing by Keyword "Detection"
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Item Deep learning to find colorectal polyps in colonoscopy: A systematic literature review: A systematic literature review(2020-08) Sánchez-Peralta, Luisa F.; Bote-Curiel, Luis; Picón, Artzai; Sánchez-Margallo, Francisco M.; Pagador, J. Blas; COMPUTER_VISIONColorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.Item Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo(2018) Picon, Artzai; Irusta Zarandona, Unai; Alvarez-Gila, Aitor; Aramendi Ecenarro, Elisabete; Garrote, Estibaliz; Ayala, Unai; Alonso-Atienza, F.; Figuera, Carlos; Tecnalia Research & InnovationDetección de fibrilación ventricular mediante técnicas de aprendizaje profundo La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red longshort- term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM.Item Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis(2021-12) Ortega-Morán, Juan Francisco; Azpeitia, Águeda; Sánchez-Peralta, Luisa F.; Bote-Curiel, Luis; Pagador, Blas; Cabezón, Virginia; Saratxaga, Cristina L.; Sánchez-Margallo, Francisco M.; VISUALBackground. The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods. Semi-structured interviews and an online survey were used. Results. Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.Item Multi-cloud applications security monitoring(Springer Verlag, 2017) Carvallo, Pamela; Cavalli, Ana R.; Mallouli, Wissam; Rios, Erkuden; Au, Man Ho Allen; Choo, Kim-Kwang Raymond; Li, Kuan-Ching; Castiglione, Arcangelo; Palmieri, Francesco; CIBERSEC&DLTThe issue of data security and privacy in multi-cloud based environments requires different solutions for implementing and enforcing security policies. In these environments, many security aspects must be faced, such as security-by-design, risk management, data privacy and isolation, and vulnerability scans. Moreover, it also becomes necessary to have a system that interrelates and operates all security controls which are configured and executed independently on each component of the application (service) being secured and monitored. In addition, thanks to the large diffusion of cloud computing systems, new attacks are emerging, so threat detection systems play a key role in the security schemes, identifying possible attacks. These systems handle an enormous volume of information as they detect unknown malicious activities by monitoring different events from different points of observation, as well as adapting to new attack strategies and considering techniques to detect malicious behaviors and react accordingly. To target this issue, we propose in the context of the MUSA EU Horizon 2020 project [1], a security assurance platform that allows monitoring the multi-cloud application deployed in different Cloud Server Providers (CSPs). It detects potential deviations from security Server Level Agreements (A formal, negotiated document that defines in quantitative and qualitative terms the service being offered to a Cloud Service Client (CSC). For more information see [8, 17].) (SLAs) and triggers countermeasures to enforce security during application runtime.Item PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets: A performance comparative of models and datasets(2020-11-28) Sánchez-Peralta, Luisa F.; Pagador, J. Blas; Picón, Artzai; Calderón, Ángel José; Polo, Francisco; Andraka, Nagore; Bilbao, Roberto; Glover, Ben; Saratxaga, Cristina L.; Sánchez-Margallo, Francisco M.; COMPUTER_VISION; VISUALColorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for effective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four different models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.Item ROCOV scheme for Fault Detection and Location in HVDC sytems(2019-07) Díaz, J.; Abarrategi, O.; Larruskain, D.M.; Perez-Basante, A.; Rubio, A.; POWER ELECTRONICS AND SYSTEM EQUIPMENTA reliable DC fault protection system is essential for the development of HVDC grids. Therefore, this paper deals with the voltage derivative ROCOV scheme to locate and detect DC faults. The algorithm is able to differentiate internal and external faults considerably fast. The proposed algorithm is analyzed in a HVDC grid with different fault case scenarios. Finally, the ROCOV protection thresholds are discussed.