Models, code, and papers for "Andre G. C. Pacheco":

Classificação de espécies de peixe utilizando redes neurais convolucional

May 09, 2019
Andre G. C. Pacheco

Data classification is present in different real problems, such as recognizing patterns in images, differentiating defective parts in a production line, classifying benign and malignant tumors, among many others. Many of these problems have data patterns that are hard to identify, which requires more advanced techniques for resolution. Recently, several works addressing different artificial neural network architectures have been applied to solve classification problems. When the classification problem must be obtained through images, currently, the standard methodology is the use of convolutional neural networks. Thus, in this report convolutional neural networks are used to classify fish species. Classifica\c{c}\~ao de dados est\'a presente em diversos problemas reais, tais como: reconhecer padr\~oes em imagens, diferenciar pe\c{c}as defeituosas em uma linha de produ\c{c}\~ao, classificar tumores benignos e malignos, dentre diversas outras. Muitos desses problemas possuem padr\~oes de dados dif\'iceis de serem identificados, o que requer, consequentemente, t\'ecnicas mais avan\c{c}adas para sua resolu\c{c}\~ao. Recentemente, diversos trabalhos abordando diferentes arquiteturas de redes neurais artificiais v\^em sendo aplicados para solucionar problemas de classifica\c{c}\~ao. Quando a classifica\c{c}\~ao do problema deve ser obtida por meio de imagens, atualmente a metodologia padr\~ao \'e uso de redes neurais convolucionais. Sendo assim, neste trabalho s\~ao utilizadas redes neurais convolucionais para classifica\c{c}\~ao de esp\'ecies de peixes.

* in Portuguese 

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The impact of patient clinical information on automated skin cancer detection

Sep 16, 2019
Andre G. C. Pacheco, Renato A. Krohling

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.

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Ranking of classification algorithms in terms of mean-standard deviation using A-TOPSIS

Oct 22, 2016
Andre G. C. Pacheco, Renato A. Krohling

In classification problems when multiples algorithms are applied to different benchmarks a difficult issue arises, i.e., how can we rank the algorithms? In machine learning it is common run the algorithms several times and then a statistic is calculated in terms of means and standard deviations. In order to compare the performance of the algorithms, it is very common to employ statistical tests. However, these tests may also present limitations, since they consider only the means and not the standard deviations of the obtained results. In this paper, we present the so called A-TOPSIS, based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), to solve the problem of ranking and comparing classification algorithms in terms of means and standard deviations. We use two case studies to illustrate the A-TOPSIS for ranking classification algorithms and the results show the suitability of A-TOPSIS to rank the algorithms. The presented approach is general and can be applied to compare the performance of stochastic algorithms in machine learning. Finally, to encourage researchers to use the A-TOPSIS for ranking algorithms we also presented in this work an easy-to-use A-TOPSIS web framework.

* 16 pages, 8 figures and 11 tables 

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Skin cancer detection based on deep learning and entropy to detect outlier samples

Sep 10, 2019
Andre G. C. Pacheco, Abder-Rahman Ali, Thomas Trappenberg

We describe our methods to address both tasks of the ISIC 2019 challenge. The goal of this challenge is to provide the diagnostic for skin cancer using images and meta-data. There are nine classes in the dataset, nonetheless, one of them is an outlier and is not present on it. To tackle the challenge, we apply an ensemble of classifiers, which has 13 convolutional neural networks (CNN), we develop two approaches to handle the outlier class and we propose a straightforward method to use the meta-data along with the images. Throughout this report, we detail each methodology and parameters to make it easy to replicate our work. The results obtained are in accordance with the previous challenges and the approaches to detect the outlier class and to address the meta-data seem to be work properly.

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Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoother

Dec 17, 2018
Smith W. A. Canchumuni, Alexandre A. Emerick, Marco Aurélio C. Pacheco

The literature about history matching is vast and despite the impressive number of methods proposed and the significant progresses reported in the last decade, conditioning reservoir models to dynamic data is still a challenging task. Ensemble-based methods are among the most successful and efficient techniques currently available for history matching. These methods are usually able to achieve reasonable data matches, especially if an iterative formulation is employed. However, they sometimes fail to preserve the geological realism of the model, which is particularly evident in reservoir with complex facies distributions. This occurs mainly because of the Gaussian assumptions inherent in these methods. This fact has encouraged an intense research activity to develop parameterizations for facies history matching. Despite the large number of publications, the development of robust parameterizations for facies remains an open problem. Deep learning techniques have been delivering impressive results in a number of different areas and the first applications in data assimilation in geoscience have started to appear in literature. The present paper reports the current results of our investigations on the use of deep neural networks towards the construction of a continuous parameterization of facies which can be used for data assimilation with ensemble methods. Specifically, we use a convolutional variational autoencoder and the ensemble smoother with multiple data assimilation. We tested the parameterization in three synthetic history-matching problems with channelized facies. We focus on this type of facies because they are among the most challenging to preserve after the assimilation of data. The parameterization showed promising results outperforming previous methods and generating well-defined channelized facies.

* 32 pages, 24 figures 

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