Longitudinal detection of radiological abnormalities with time-modulated LSTM

Convolutional neural networks (CNNs) have been successfully employed in recent years for the detection of radiological abnormalities in medical images such as plain x-rays. To date, most studies use CNNs on individual examinations in isolation and discard previously available clinical information. In this study we set out to explore whether Long-Short-Term-Memory networks (LSTMs) can be used to improve classification performance when modelling the entire sequence of radiographs that may be available for a given patient, including their reports. A limitation of traditional LSTMs, though, is that they implicitly assume equally-spaced observations, whereas the radiological exams are event-based, and therefore irregularly sampled. Using both a simulated dataset and a large-scale chest x-ray dataset, we demonstrate that a simple modification of the LSTM architecture, which explicitly takes into account the time lag between consecutive observations, can boost classification performance. Our empirical results demonstrate improved detection of commonly reported abnormalities on chest x-rays such as cardiomegaly, consolidation, pleural effusion and hiatus hernia.Comment: Submitted to 4th MICCAI Workshop on Deep Learning in Medical Imaging Analysi

Controlling and Assisting Activities in Social Virtual Worlds

Since its beginning, web technology has advanced from a text-based to a visual-based interaction. This evolution has been facilitated by both high speed internet connections and PC's graphical power. Virtual world (VW) technology began as standalone applications (e.g.. virtual simulations) but soon evolved into web-based applications. Nowadays, home users for entertainment and wide-spread enterprises or institutions for business can exploit virtual worlds to develop remote activities between friends, employees, clients, teachers or students (Sherman, 2002). Then, virtual worlds have clear applications in e-governance, elearning and e-commerce, and therefore it is mandatory to study mechanisms ensuring the assistance and the control of activities taking place in these applications..

Unsupervised learning as a complement to convolutional neural network classification in the analysis of saccadic eye movement in spino-cerebellar ataxia type 2

IWANN es un congreso internacional que se celebra bienalmente desde 1991. Su campo de estudio se centra en la fundamentación y aplicación de las distintas técnicas de Inteligencia Computacional : Redes Neuronales Artificiales, Algoritmos Genéticos, Lógica Borrosa, Aprendizaje Automático. En esta edición han participado 150 investigadores.This paper aims at assessing spino-cerebellar type 2 ataxiaby classifying electrooculography records into registers corresponding to healthy, presymptomatic and ill individuals. The primary used technique is the convolutional neural network applied to the time series of eye movements, called saccades. The problem is exceptionally hard, though, because the recorded saccadic movements for presymptomatic cases often do not substantially di er from those of healthy individuals. Precisely this distinction is of the utmost clinical importance, since early intervention on presymptomatic patients can ameliorate symptoms or at least slow their progression. Yet, each register contains a number of saccades that, although not consistent with the current label, have not been considered indicative of another class by the examining physicians. As a consequence, an unsupervised learning mechanism may be more suitable to handle this form of misclassi cation. Thus, our proposal introduces the k-means approach and the SOM method, as complementary techniques to analyse the time series. The three techniques operating in tandem lead to a well performing solution to this diagnosis problem.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Universidad de Granada, Universitat Politècnica de Catalunya, Universidad de Las Palmas de Gran Canaria, Springe

Fitting in a complex chi^2 landscape using an optimized hypersurface sampling

Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite of its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barriers when necessary, is demonstrated with synthetic functions and with real data

Maquinària amb nom de dona. Estudi i documentació en perspectiva de gènere del fons industrial del Museu del Calçat i de la Pell

[cat]El Museu del Calçat i de la Pell compta amb un fons singular, entenent-se com l’única institució museística dedicada, en la seva totalitat, al patrimoni i la memòria industrial de les Illes Balears. No obstant això, en la discursiva general, manca una anàlisi detallada del paper que va tenir la dona obrera dins la indústria. A Mallorca, la història industrial des d’una perspectiva de gènere compta amb alguns estudis molt valuosos que permeten aproximar-se a la globalitat del fenomen. És gràcies a aquests que s’evidencia una manca d’investigació en la temàtica industrial des d’una perspectiva de gènere, i encara més quan es plasma al camp de l’estudi dels béns patrimonials conservats. En aquest sentit, l’article proposa visibilitzar i posar en valor el rol de la dona dins la indústria del calçat a Mallorca a través de l’estudi i la documentació dels béns mobles conservats al Museu del Calçat i de la Pell.[eng]The Footwear and Leather Museum has a unique collection. It is understood as the only institution which is entirely dedicated to the industrial heritage of the Balearic Islands. However, there isn’t a detailed analysis of the women workers role’s in the industry. Mallorca has some very valuable studies about industrial history from a gender perspective that allows to approach the whole phenomenon. However, there is a lack of research in the industrial subject from a gender perspective, and even more, when it is reflected in the heritage studies. In this way, the article proposes to make visible and emphasize the women role in the footwear mallorcan industry, through the study and documentation of the moveable cultural heritage preserved at the Footwear and Leather Museum

Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.Comment: 9 pages, 2 figure