Browsing by Author "Mauricio, Antoni"

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    A deep learning approach for sentiment analysis in Spanish Tweets
    (Springer Verlag, 2018) Vizcarra Aguilar, Gerson; Mauricio, Antoni; Mauricio, Leonidas
    Sentiment Analysis at Document Level is a well-known problem in Natural Language Processing (NLP), being considered as a reference in NLP, over which new architectures and models are tested in order to compare metrics that are also referents in other issues. This problem has been solved in good enough terms for English language, but its metrics are still quite low in other languages. In addition, architectures which are successful in a language do not necessarily works in another. In the case of Spanish, data quantity and quality become a problem during data preparation and architecture design, due to the few labeled data available including not-textual elements (like emoticons or expressions). This work presents an approach to solve the sentiment analysis problem in Spanish tweets and compares it with the state of art. To do so, a preprocessing algorithm is performed based on interpretation of colloquial expressions and emoticons, and trivial words elimination. Processed sentences turn into matrices using the 3 most successful methods of word embeddings (GloVe, FastText and Word2Vec), then the 3 matrices merge into a 3-channels matrix which is used to feed our CNN-based model. The proposed architecture uses parallel convolution layers as k-grams, by this way the value of each word and their contexts are weighted, to predict the sentiment polarity among 4 possible classes. After several tests, the optimal tuple which improves the accuracy were <1, 2>. Finally, our model presents %61.58 and %71.14 of accuracy in InterTASS and General Corpus respectively. © Springer Nature Switzerland AG 2018.
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    Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images
    (Springer Verlag, 2017) García Chávez, Gabriel Enrique; Gallardo, Jhair; Mauricio, Antoni; López, Jorge; Del Carpio, Christian
    Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process. © Springer International Publishing AG 2017.
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    High-resolution generative adversarial neural networks applied to histological images generation
    (Springer Verlag, 2018) Mauricio, Antoni; López, Jorge; Huauya, Roger; Diaz Rosado, Jose Carlos
    For many years, synthesizing photo-realistic images has been a highly relevant task due to its multiple applications from aesthetic or artistic [19] to medical purposes [1, 6, 21]. Related to the medical area, this application has had greater impact because most classification or diagnostic algorithms require a significant amount of highly specialized images for their training yet obtaining them is not easy at all. To solve this problem, many works analyze and interpret images of a specific topic in order to obtain a statistical correlation between the variables that define it. By this way, any set of variables close to the map generated in the previous analysis represents a similar image. Deep learning based methods have allowed the automatic extraction of feature maps which has helped in the design of more robust models photo-realistic image synthesis. This work focuses on obtaining the best feature maps for automatic generation of synthetic histological images. To do so, we propose a Generative Adversarial Networks (GANs) [8] to generate the new sample distribution using the feature maps obtained by an autoencoder [14, 20] as latent space instead of a completely random one. To corroborate our results, we present the generated images against the real ones and their respective results using different types of autoencoder to obtain the feature maps. © Springer Nature Switzerland AG 2018.