Alexei M. C. Machado

• Professor (Associate) at Pontifical Catholic University of Minas Gerais

Introduction Prostate cancer (PC) remains a significant global health concern, with prognostic assessments largely reliant on the Gleason Classification System. While it has proven effective, subjectivity in interpretation persists, prompting the need for complementary approaches. Reactive stroma (RS) has emerged as a potential candidate for enhanc...

In recent years, deep learning has achieved formidable results in creative computing. When it comes to music, one viable model for music generation are Transformer based models. However, while transformers models are popular for music generation, they often rely on annotated structural information. In this work, we inquire if the off-the-shelf Musi...

Este artigo investiga o problema de apoio ao diagnóstico de câncer de colo de útero por meio da aplicação de aprendizado profundo para análise de células do exame Papanicolau. O trabalho apresenta um processo para classificação de células, além dos resultados de diferentes modelos convolucionais na tarefa de classificação.

Resumo Objetivo: Desenvolver um modelo computacional - rede neural convolucional (RNC) - treinado com radiografias da linha de base do Estudo Longitudinal de Saúde do Adulto Musculoesquelético (ELSA-Brasil Musculoesquelético), para a classificação automática de osteoartrite dos joelhos. Materiais e Métodos: Trata-se de um estudo transversal abrange...

Objective To develop a convolutional neural network (CNN) model, trained with the Brazilian “Estudo Longitudinal de Saúde do Adulto Musculoesquelético” (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods This was a...

This work presents an implementation of the Transformer on the problem of predicting stock prices from time series. The model is compared with ARIMA and a neural network with LSTM cells. We hypothesize that, due to the powerful memory capacity and association between series values, the Transformer would be able to achieve better results than other...

Monitoring wound healing is a necessary procedure to help health services control pressure ulcers. The correct diagnosis depends on clinical observations by doctors and nurses during patient visits. The evaluation of the wound area represents one of the most important data. Usually, health professionals assess ulcers through visual inspection, usin...

A classificação da densidade mamária através de exames de raio X ainda é considerado o principal mecanismo para detecção precoce do câncer de mama, uma vez que o tecido fibroglandular pode esconder tumores iniciais. O objetivo deste artigo é propor um modelo interpretável para a classificação de densidades mamárias em imagens mamográficas. A arquit...

Pneumonia is a lung disease responsible for the highest number of deaths from infection in children and adults. Its diagnosis must be fast and accurate so that procedures are taken as soon as possible to combat the disease. In this work, Convolutional Neural Networks were explored for the classification of chest radiography images in the context of...

A detecção precoce de linfonodos malignos é crítica para o tratamento do câncer de tireoide. Neste estudo, um sistema de diagnóstico é proposto para classificar nódulos malignos com base em imagens de ultrassom, bem como na escala do Thyroid Imaging Reporting and Data System (TI-RADS). Os experimentos implementam 5 redes convolucionais e 3 máquinas...

The weighting of sub-indicators is widely debated in the composite indicator literature. However, these weighting schemes’ effects on the composite indicator’s spatial dependence property are still little known. This research reveals a direct relationship between the weighting scheme of sub-indicators and the spatial autocorrelation of the composit...

The weighting of sub-indicators is a controversial topic in the literature, being the object of investigation by many researchers. The absence of a clear definition for the most appropriate sub-indicator weighting scheme has led researchers to combine subjective and objective weighting schemes. In this work, an innovative sub-indicator weighting sc...

Principal component analysis (PCA) is a popular technique for building social indicators in the field of spatial analysis. However, literature shows that there is no consensus on how to apply PCA to longitudinal studies, and researchers have done the analysis using different approaches, varying the way data are combined and the frequency in which t...

Introduction Although the diurnal fluctuation of motor dysfunction, reversible with small doses of dopamine, is a cornerstone for the phenotype of the autosomal dominant Segawa syndrome, the non-motor symptoms of this neurotransmitter deficiency have still received limited attention. Objective This study aims to evaluate non-motor symptoms of this...

Composite indicators are one-dimensional measures of multidimensional phenomena. Through the composite indicators, it is possible to have a single map of the different subindicators of poverty, inequality, sustainability, and economic development. This research employs two well-known methods of building composite indicators to represent the social...

Introduction Mother–child interactions during the first years of life have a significant impact on the emotional and cognitive development of the child. In this work, we study how a prenatal diagnosis of malformation may affect maternal representations and the quality of these early interactions. To this end, we conducted a longitudinal observation...

Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate throu...

Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the abilit...

This research explores the relationship between the implicit importance of the variables of a multidimensional phenomenon within the context of urban inequalities and the weights attributed to these variables during the process of building a composite indicator (CI) by data-based weighting methods, such as principal component analysis (PCA). The ob...

A previsão do consumo de eletricidade é essencial para as concessionárias de distribuição de energia elétrica uma vez que pode auxiliar, principalmente, nas decisões relacionadas ao core business dessas empresas. Este trabalho apresenta modelos lineares para a previsão de demanda de energia elétrica, a curto e médio prazos, de uma concessionária. O...

Background and objective: Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment techniques that include the use of rulers and adhesive labels in direct contact with the injury, be...

Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted-or not-as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and con...

Chest X-Rays are the most common type of biomedical radiologic exam, being widely adopted for the diagnosis of a myriad of illnesses in the thoracic region. Computed Tomography – even though being more expensive and rare – is also a useful tool for the detection of several illnesses and surgery planning, providing volumetric information. This paper...

Neste estudo, avaliou-se o potencial das redes neurais convolucionais na classificação de texturas para diagnóstico de câncer de mama na escala BI-RADS de quatro níveis. A base de dados foi constituída de 5024 exames mamográficos, com recortes de 128x128 pixels. As escalas foram avaliadas em dois conjuntos, o primeiro agrupando as escalas não-densa...

Pectoral muscle and background elimination are common steps for automated software in mammographic image preprocessing. We investigate FCNs, U-nets and SegNets in the task of mammogram segmentation, addressing three subtasks: pectoral muscle, background and breast region segmentation. The MIAS and INbreast datasets were used for evaluating Deep Neu...

In Brazil, the government has historically given low attention to telecommunication infrastructure planning to predict, for instance, the Internet bandwidth in the short and medium-term, since this process can be slow and costly. Notably, smart city applications are impaired by this policy because they depend on cost-benefit technology to support t...

Raman spectroscopy is widely used to investigate the structure and property of the molecules from their vibrational transitions and may allow for the diagnosis of cancer in a fast, objective, and nondestructive manner. This experimental study aims to propose the use of the 1064-nm wavelength laser in a Raman spectroscopy and to evaluate its discrim...

Studies indicate that breast density is related to the risk of developing cancer since dense breast tissue can hide lesions, causing cancer to be detected at later stages. In this paper we classification method using support vector machines (SVM) associated to data reduction techniques to classify mammographic texture. An analysis of the parameters...

Pressure ulcers are skin lesions caused by the excessive compression of soft tissues between bones and hard surfaces that may increase treatment risks and costs. Manual techniques to evaluate the area of the affected region include the use of adhesive labels and rulers in direct contact with the ulcer. In this paper, a semi‐automatic method is prop...

Improving the degree of assistance given by in-car navigation systems is an important issue for the safety of both drivers and passengers. There is a vast body of research that assesses the usability and interfaces of the existing navigation systems but very few investigations study the impact on the brain activity based on navigation-based driving...

Introduction: The standard treatment for locally advanced rectal cancer (RC) consists of neoadjuvant chemoradiation followed by radical surgery. Regardless the extensive use of SUVmax in ¹⁸F-FDG PET tumor uptake as representation of tumor glycolytic consumption, there is a trend to apply metabolic volume instead. Thus, the aim of the present study...

Task switching is a common method to investigate executive functions such as working memory, attention, etc. This study investigates the effect of acute stress on brain activity using task switching. Surprisingly few studies have been conducted in this area There is behavioral and physiological evidence to indicate that acute stress makes the parti...

Purpose: Retrospective study of the effects of anticancer treatment on the brain metabolism of patients diagnosed with rectal cancer based on a large and homogeneous sample of 40 paired F-FDG PET/CT volumes taken from 20 patients. The results are compared to the ones presented by related works to help elucidating the mechanisms of neurotoxicity as...

In this paper, we propose a method for solving constrained optimization problems using Interval Analysis combined with Particle Swarm Optimization. A Set Inverter Via Interval Analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty sea...

Remote Sensing Images are one of the main sources of information about the earth surface. They are widely used to automatically generate thematic maps that show the land cover of an area. This process is traditionally done by using supervised classifiers which learn patterns extracted from the image pixels annotated by the user and then assign a la...

Red light running is a very common traffic violation. Nowadays, vehicles running red traffic lights are detected by sensors fixed on the streets. However a very small percentage of all traffic lights is equipped with such sensors. For this reason, this work proposes a red light running detection system that analyzes the video captured by a camera i...

In this work we propose an integrated approach for the design of humanoid robots, whose solution involves a simple control strategy that results on lower costs for large-scale production. A novel concept of organizational structure called Amorphic Architecture is presented. It allows for the deployment of hybrid controller systems and enables the g...

People detection and tracking in video sequences are a crucial step for many applications such as security systems and entertainment. Although humans can easily perform these tasks, detecting and tracking people in dynamic background scenes are not trivial for computer vision systems. Furthermore, the amount of data generated by these applications...

A Análise de Discriminantes Lineares de Duas Classes (2C-LDA) é uma técnica de extração de características baseada na Análise de Discriminantes Lineares (LDA) que apresenta alta taxa de acerto quando aplicada ao problema de reconhecimento de faces. Essa técnica, no entanto, não pode ser aplicada em cenários onde somente uma amostra por pessoa está...

Accurate delineation of tumors is a fundamental requirement for proper planning and subsequent cancer treatment. In this paper, we propose to model the process of tumor segmentation as a multicriteria decision making problem, considering the information embedded in both Positron Emission Tomography (PET) and Computed tomography (CT) images. A set o...

The validation of the results obtained by hypothesis testing is of special interest in applications that deal with high-dimensional sets of variables. The use of equivocated statistical methods may result in poor control of false positives. On the other hand, overconservative methods may prevent relevant findings. In this paper we define dependence...

Automatic face recognition systems have currently reached high hit rates. Nevertheless, simple steps like image registration are not being considered in several methods. The alignment of the set of images in a same coordinated system must be seen as an initial and crucial step in algorithms that are based on dimensionality reduction. This work aims...

In this paper, we present a content-based image retrieval system designed to retrieve mammographies from large medical image database. The system is developed based on breast density, according to the four categories defined by the American College of Radiology, and is integrated to the database of the Image Retrieval in Medical Applications (IRMA)...

Multiple hypothesis testing in biomedical signal analysis and genomic signal processing has been facing a difficult dilemma regarding the adjustment of significance values. Studies that report unadjusted values may fail to control false positives. On the other hand, classical correction methods may be too conservative while handling high-dimensiona...

Modern non-invasive imaging modalities such as Computer-Aided Tomography and Magnetic Resonance Imaging brought a new perspective to anatomic characterization, as they allowed detailed observation of in vivo structures. The amount of provided data became, however, progressively overwhelming. Data may be useless if we are not able to extract relevan...

In this paper we propose a set of methods to describe, register and retrieve images of elongated structures from a database based on their shape content. Registration is performed based on an elastic matching algorithm that jointly takes into account the gross shape of the structure and the shape of its boundary, resulting in anatomically consisten...

This work aims at proposing a set of methods to describe, register and retrieve images of elongated structures from a database based on their shape content. We propose a registration algorithm that jointly takes into account the gross shape of the structure and the shape of its boundary, resulting in anatomically consistent deformations. The method...

We present a novel method for correcting the significance level of hypothesis testing that requires multiple comparisons. It is based on the spectral graph theory, in which the variables are seen as the vertices of a complete undirected graph and the correlation matrix as the adjacency matrix that weights its edges. The method increases the statist...

In this paper, novel methods were used to map the corpus callosum morphology of children with chromosome 22q11.2 deletion syndrome in order to further investigate changes to that structure and to examine their possible effects on cognitive function. The callosal profiles were extracted from the centermost MRI midsagittal slice by supervised thresho...

In this work, we present a parallel implementation of an algorithm that simulates the natural convection of fluids in a closed cavity. The method is based on the Boussinesq approximation; and it is numerically solved using the finite volume method, based on the Power-Law interpolating scheme. The pressure-velocity coupling is solved using SIMPLEC a...

In this paper, we present a novel method for estimating the effective number of independent variables in imaging applications that require multiple hypothesis testing. The method increases the statistical power of the results by re- futing the assumption of independence among variables, while keeping the probability of false positives low. It is ba...

We present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori...

This article presents a new method for discovering hidden patterns in high-dimensional dataset resulting from image registration. It is based on true factor analysis, a statistical model that aims to find clusters of correlated variables. Applied to medical imaging, factor analysis can potentially identify regions that have anatomic significance an...

This article presents a novel method for visual data mining based on exploratory factor analysis. Modern imaging modalities provide an overwhelming amount of information that cannot be effectively handled without computerized tools. Data mining techniques aim to discover new knowledge from the collected data and to statistically represent this know...

Unlabelled: This article presents an exploratory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to knowledge discovery a...

We present a likelihood model for Bayesian nonrigid image registration that relates the distinct acquisition models of different MRI (magnetic resonance imaging) scanners. The model is derived from a Bayesian network that represents the imaging situation under consideration to construct the appropriate similarity measure for the given situation. Th...

We present a new perspective to image retrieval based on multivariate factor analysis. Modern imaging modalities provide an overwhelming amount of information that cannot be appropriately handled without computerized tools. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made...

This article presents a method for the segmentation of substructures based on exploratory factor analysis. In this approach, a set of high-dimensional shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insigh...

In this work, we present a method for exploring the relationship among morphometric variables and the possible anatomic significance of these relationships. The analysis is based on the Jacobian determinant field resulting from the registration of a template to a set of subjects, which is represented as a factorial analytic model. In addition to mo...

This paper presents a factor analytic approach to morphometry in which strong intercorrelations among a high-dimensional set of shape-related variables are sought. The correlated variables potentially correspond to substructures of anatomy and thus have a natural interpretation. The analysis is based on information about the pointwise size differen...

Presents an exploratory and confirmatory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to morphometric investigations. I...

In this work, we present a method which is able to relate different MR sensors with respect to intensity distortions in the output images. For the important problem of image registration, the method makes possible a principled approach to likelihood modeling or the construction of similarity metrics. Likelihood models can be used as prior knowledge...

In this paper, we present an exploratory factor analytic approach to morphometry in which a high-dimensional set of shape-related variables is examined with the purpose of finding clusters with strong correlation. This clustering can potentially identify regions that have anatomic significance and thus lend insight to the morphometric investigation...

A method is presented for determining the intensity mapping between MRI images that may have been acquired using different sequences or instruments. The method can be applied to fully elastic matching and produces spatially localized probability functions that are capable of representing in an efficient way strong intensity distortions due, for ins...

The problem of matching two images can be posed as the search for a displacement field which assigns each point of one image to a point in the second image in such a way that a likelihood function is maximized ruled by topological constraints. Since the images may be acquired by different scanners, the intensity relationship between intensity level...

The problem of matching two images can be posed as finding a displacement field which assigns each point of the reference image to a point in the test image. In this paper we present an iterative algorithm to estimate the probability density function relating the intensity distribution of two MR scanners, based on the topological constraints embedd...

In this work, we describe an automated approach to morphometry based on spatial normalizations of the data, and demonstrate its application to the analysis of gender differences in the human corpus callosum. The purpose is to describe a population by a reduced and representative set of variables, from which a prior model can be constructed. Our app...

In this paper we discuss the application of spatial-domain filters for solving the problem of automatic lane detection on gel electrophoresis computer images. The problem can be posed as the determination of the number, location and orientation of lanes on the image, based on the analysis of their gray-level intensities. A novel iterative filtering...

In this paper we discuss the application of spatial-domain filters for solving the problem of automatic lane detection on gel electrophoresis computer images. The problem can be posed as the determination of the number, location and orientation of lanes on the image, based on the analysis of their gray-level intensities. A novel iterative filtering...

The development of adaptive systems must face the problem of recognition as a synergy of learning and knowledge. This paper presents a method for constructing influence diagrams from backpropagation neural networks, as a way of combining the main advantages of these methodologies. The basic concepts of influence diagrams and neural networks are dis...

This paper discusses the application of Influence Diagrams on training Neural Networks. The basic concepts of these two methodologies are presented as a brief review. The conventional back-propagation training procedure is compared to other alternatives, by means of an example on visual pattern recognition.