This work presents an alternative method to represent documents based on LDA (Latent Dirichlet Allocation) and how it affects to classification algorithms, in comparison to common text ...representation. LDA assumes that each document deals with a set of predefined topics, which are distributions over an entire vocabulary. Our main objective is to use the probability of a document belonging to each topic to implement a new text representation model. This proposed technique is deployed as an extension of the Weka software as a new filter. To demonstrate its performance, the created filter is tested with different classifiers such as a Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Naive Bayes in different documental corpora (OHSUMED, Reuters-21578, 20Newsgroup, Yahoo! Answers, YELP Polarity, and TREC Genomics 2015). Then, it is compared with the Bag of Words (BoW) representation technique. Results suggest that the application of our proposed filter achieves similar accuracy as BoW but greatly improves classification processing times.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image ...generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.
Image generative models have advanced in many areas to produce synthetic images of high resolution and detail. This success has enabled its use in the biomedical field, paving the way for the ...generation of videos showing the biological evolution of its content. Despite the power of generative video models, their use has not yet extended to time-based development, focusing almost exclusively on generating motion in space. This situation is largely due to the lack of specific data sets and metrics to measure the individual quality of videos, particularly when there is no ground truth available for comparison. We propose a new dataset, called GoldenDOT, which tracks the evolution of apples cut in parallel over 10 days, allowing to observe their progress over time while remaining static. In addition, four new metrics are proposed that provide different analyses of the generated videos as a whole and individually. In this paper, the proposed dataset and measures are used to study three state of the art video generative models and their feasibility for video generation with biological development: TemporalGAN (TGANv2), Low Dimensional Video Discriminator GAN (LDVDGAN), and Video Diffusion Model (VDM). Among them, the TGANv2 model has managed to obtain the best results in the vast majority of metrics, including those already known in the state of the art, demonstrating the viability of the new proposed metrics and their congruence with these standard measures.
Nowadays, trends in deep learning for text classification are addressed to create complex models to deal with huge datasets. Deeper models are usually based on cutting edge neural network ...architectures, achieving good results in general but demanding better hardware than shallow ones. In this work, a new Convolutional Neural Network (CNN) architecture (MobyDeep) for text classification tasks is proposed. Designed as a configurable tool, resultant models (MobyNets) are able to manage big corpora sizes under low computational costs. To achieve those milestones, the architecture was conceived to produce lightweight models, having their internal layers based on a new proposed convolutional block. That block was designed and customized by adapting ideas from image to text processing, helping to squeezing model sizes and to reduce computational costs. The architecture was also designed as a residual network, covering complex functions by extending models up to 28 layers. Moreover, middle layers were optimized by residual connections, helping to remove fully connected layers on top and resulting in Fully CNN. Corpus were chosen from the recent literature, aiming to define real scenarios when comparing configured MobyDeep models with other state-of the-art works. Thus, three models were configured in 8, 16 and 28 layers respectively, offering competitive accuracy results.
We present a case of herniation of the left atrial appendage through a congenital partial absence of the pericardium. The diagnosis was demonstrated by echocardiography and the surgical correction ...achieved with autologous pericardium.
Embolization of the systemic arteries of the lung (described by Remy and colleagues in 1973) is now a useful method for the treatment of hemoptysis or hemorrhagic lesions of the lung prior to ...surgical treatment, or for local treatment of hemoptysis when surgery is contraindicated or unnecessary. The technique is based on the anatomy of the different divisions of the systemic circulation (bronchial and extrabronchial), which for various physiologic reasons may develop hypervascularization. The results, complications, and contraindications of systemic embolization have previously been described and the technique is now commonly practiced.
The diagnosis of diaphragmatic tumours is complicated by their rarity and because they are often difficult to distinguish from the more frequent tumours of surrounding structures. We describe two ...cases: an invasive fibroma and a primary hydatid cyst. We discuss the differential diagnosis of diaphragmatic tumours and suggest a systematic approach to their radiological diagnosis, stressing the value of ultrasonography, CT scanning and diagnostic pneumoperitoneum.
In 200 young patients with apparently idiopathic spontaneous pneumothorax, the following radiologic features were analyzed: degree of collapse on the initial chest film, areas of atelectasis, and ...presence of blebs, apical opacities, fibrous adhesions, pleural effusions, and controlateral shift of mediastinal structures. Confrontation of apical changes with pathologic findings in operative specimens suggests that mesothelial rupture with reactive hyperplasia results in a "pneumatization chamber" visible as a bullous image. Following drainage, homolateral shifts of mediastinum and four cases of pulmonary edema were recorded. Risk factors for pulmonary edema include severe pulmonary collapse with areas of atelectasis, persisting for more than 48 hours and an aspiration which either exceeded 1.5 l. of air or was performed with a depression of more than 30 cm of water.
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