Arquitecturas Flexibles, Crecientes y Jerárquicas para Sistemas Neuronales Autoorganizados

Conferencia impartida por el profesor Esteban José Palomo Ferrer.La autoorganización es un proceso de aprendizaje no supervisado mediante el cual se descubren características, relaciones, patrones significativos o prototipos en los datos. Entre los sistemas neuronales autoorganizados más usados destaca el el mapa autoorganizado o SOM (Self-Organizing Map), el cual ha sido aplicado en multitud de campos distintos. Sin embargo, este modelo autoorganizado tiene varias limitaciones relacionadas con su tamaño, topología, falta de representación de relaciones jerárquicas, etc. La red neuronal llamada gas neuronal creciente o GNG (Growing Neural Gas), es un ejemplo de modelo neuronal autoorganizado con mayor flexibilidad que el SOM ya que está basado en un grafo de unidades de proceso en vez de en una topología fija. A pesar de su éxito, se ha prestado poca atención a su extensión jerárquica, a diferencia de muchos otros modelos que tienen varias versiones jerárquicas. El gas neuronal jerárquico creciente o GHNG (Growing Hierarchical Neural Gas) es una extensión jerárquica del GNG en el que se aprende un árbol de grafos, donde el algoritmo original del GNG se ha mejorado distinguiendo entre una fase de crecimiento y una fase de convergencia. Los resultados experimentales demuestran las capacidades de autoorganización y aprendizaje jerárquico de esta red.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Deep learning for coronary artery disease severity classification

Medical imaging evaluations are one of the fields where computed-aid diagnosis could improve the efficiency of diagnosis supporting physician decisions. Cardiovascular Artery Disease (CAD) is diagnosed using the gold standard, Invasive Coronary Angiography (ICA). In this work, performance analysis for binary classification of ICA images considering the severity ranges separately is reported, evaluating how performance is affected depending on the degree of lesions considered. For this purpose, an annotated dataset of ICA images was employed, which contains the ground truth, the location and the category of lesions into seven possible ranges: <20 %, [20 %, 49 %], [50 %, 69 %], [70 %, 89 %], [90 %, 98 %], 99 %, and 100 %. The ICA images were pre-processed, divided into patches and balanced by downsampling and data augmentation. In this study, four known pre-trained CNN architectures were trained using different categories of lesion degree as input, whose F-measures are computed. Results report that the F-measures showed a behavior dependent on the narrow presents of the image, being lesions with more than 50 % severity were better classified, achieving an F-measure of 75%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Parallel proccessing applied to object detection with a Jetson TX2 embedded system.

Video streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform object detection. In this paper, we report a host of improvements to our previous state-of-the-art software system to reliably detect and track objects in video streams from panoramic cameras, resulting in an increase in the processing framerate in a Jetson TX2 board, with respect to our previous results. Depending on the number of processes and the load profile, we observe up to a five-fold increase in the framerate.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A novel continual learning approach for competitive neural networks

Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few works have considered continual learning for unsupervised learning methods. In this paper, a novel approach to provide continual learning for competitive neural networks is proposed. To this end, we have proposed a different learning rate function that can cope with non-stationary distributions by adapting the model to learn continuously. Experimental results performed with different synthetic images that change over time confirm the performance of our proposal.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Prosthetic Valve Candida spp. Endocarditis: New Insights Into Long-term Prognosis—The ESCAPE Study

International audienceBackground: Prosthetic valve endocarditis caused by Candida spp. (PVE-C) is rare and devastating, with international guidelines based on expert recommendations supporting the combination of surgery and subsequent azole treatment.Methods: We retrospectively analyzed PVE-C cases collected in Spain and France between 2001 and 2015, with a focus on management and outcome.Results: Forty-six cases were followed up for a median of 9 months. Twenty-two patients (48%) had a history of endocarditis, 30 cases (65%) were nosocomial or healthcare related, and 9 (20%) patients were intravenous drug users. "Induction" therapy consisted mainly of liposomal amphotericin B (L-amB)-based (n = 21) or echinocandin-based therapy (n = 13). Overall, 19 patients (41%) were operated on. Patients <66 years old and without cardiac failure were more likely to undergo cardiac surgery (adjusted odds ratios [aORs], 6.80 [95% confidence interval [CI], 1.59-29.13] and 10.92 [1.15-104.06], respectively). Surgery was not associated with better survival rates at 6 months. Patients who received L-amB alone had a better 6-month survival rate than those who received an echinocandin alone (aOR, 13.52; 95% CI, 1.03-838.10). "Maintenance" fluconazole therapy, prescribed in 21 patients for a median duration of 13 months (range, 2-84 months), led to minor adverse effects.Conclusion: L-amB induction treatment improves survival in patients with PVE-C. Medical treatment followed by long-term maintenance fluconazole may be the best treatment option for frail patients

Role of age and comorbidities in mortality of patients with infective endocarditis.

The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups: A total of 3120 patients with IE (1327  There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in th