Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results with the FIOCRUZ data set in a binary classification (lesion or normal). Our deepbased classifier outperformed the state-of-the-art results on the same data set.Additionally, classification of hypercellularity sub-lesions was also performed, considering mesangial, endocapilar and both lesions; in this multi-classification task, our proposed method just failed in 4% of the cases. To the best of our * First two authors contributed equally.knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.

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Aprendizagem Baseada em Problemas (PBL-Problem-Based Learning) é um método de ensino ativo, centrado no aluno, que se apresenta como uma abordagem adequada ao ensino de engenharia, devido ao processo de auto-aprendizagem, ao desenvolvimento de habilidades sociais e à resolução de problemas da vida real. Este artigo descreve a aplicação do PBL em um componente curricular integrado de programação de computadores, reunindo os componentes de estrutura de dados, programação orientada a objetos e matemática discreta. As opiniões dos professores e de alunos sobre os processos de ensino e aprendizagem foram coletadas com o objetivo de avaliar o componente curricular integrado e os resultados de aprendizagem. Tais resultados indicam que o método PBL pode ser aplicado com sucesso para o ensino e a aprendizagem em um componente integrado de programação de computadores.

Este artigo apresenta o desenvolvimento de um framework que contém funções para implementar as análises cefalométricas mais usadas em clínicas de radiologia odontológica. O objetivo deste framework é receber como parâmetros de entrada as coordenadas dos pontos cefalométricos, a imagem radiográfica e a análise cefalométrica escolhida. E, após processar estes dados, calcular a distância, ângulos e bissetrizes, e gerar um relatório contendo todas essas medidas e desenhar o traçado cefalométrico correspondente. Utilizando o framework foram implementadas treze Análises Cefalométricas no Odontoradiosis: Steiner, Downs, Wylie, Tweed, Harvold, McNamara, USP-Unicamp, Ricketts, Resumida de Ricketts, Jarabak, Unesp/Araraquara, USP e Unicamp. Todas as regras de cada análise cefalométrica foram implementadas, sempre com o auxílio de especialistas da área de odontologia. Em seguida, as análises de McNamara e de Tweed foram validadas. Esta validação se deu através da realização das análises, de forma manual, e através da ferramenta Odontoradiosis por um especialista da área de odontologia. Em seguida, foram feitas as comparações dos resultados obtidos através da marcação manual e através do software. Para esta validação foram utilizadas cinco radiografias e através da análise dos resultados pôde-se verificar resultados discrepantes para alguns valores das duas análises validadas quando comparados os dados da marcação manual em relação a marcação realizada pelo software. Assim, pôde-se concluir que todas as análises cefalométricas implementadas necessitam passar por uma validação com o auxílio de um especialista da área de odontologia, e que a partir das regras das treze Análises Cefalométricas já implementadas no framework, a inserção de novas análises será facilitada, pois mesmo que uma análise cefalométrica possua alguma característica diferente, será necessária a codificação apenas desta particularidade no framework como uma nova regra.

As all women over the age of 40 are recommended to perform mammographic exams every two years, the demands on radiologists to evaluate mammographic images in short periods of time has increased considerably. As a tool to improve quality and accelerate analysis CADe/Dx (computer-aided detection/diagnosis) schemes have been investigated, but very few complete CADe/Dx schemes have been developed and most are restricted to detection and not diagnosis. The existent ones usually are associated to specific mammographic equipment (usually DR), which makes them very expensive. So this paper describes a prototype of a complete mammography CADx scheme developed by our research group integrated to an imaging quality evaluation process. The basic structure consists of pre-processing modules based on image acquisition and digitization procedures (FFDM, CR or film + scanner), a segmentation tool to detect clustered microcalcifications and suspect masses and a classification scheme, which evaluates as the presence of microcalcifications clusters as well as possible malignant masses based on their contour. The aim is to provide enough information not only on the detected structures but also a pre-report with a BI-RADS classification. At this time the system is still lacking an interface integrating all the modules. Despite this, it is functional as a prototype for clinical practice testing, with results comparable to others reported in literature.

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-The classification of breast density is very subjective even for the experts, the categories 2 and 3, in many cases are confused. Thus, the objective of this study is to evaluate the influence of the sigmoid function in the density classification of images with lesions by using texture attributes. It was used 28 images with lesion, 19 belong to the category 2 (P2 -partially fat) and 9 to category 3 (P3 -dense). The sigmoid function was implemented and applied to all images to contrast windowing. A set of 14 Haralick descriptors were implemented. After the attributes extraction step it used the clustering technique K-Means to classify the category images of breast density 2 and 3. Seven of the 14 Haralick descriptors (energy/uniformity, contrast, variance/homogeneity, average sum, variance of the sum, entropy difference and maximum correlation coefficient) showed higher success rate when used images processed with sigmoid function. However, five attributes (correlation, entropy sum, difference variance, correlation information measured 1 and 2) presented classification results below those that results by using the original images, and two attributes (inverse difference moment and entropy) obtained the same results, for classification of both images (images with sigmoid function and original images). The attributes combination used to classify images with sigmoid function were better and the combination that had the best classification accuracy rate was the contrast and variance attributes. The use of the sigmoid function directly influenced the results of classification in the categories 2 and 3, however, when it was used by only one attribute in the classification, not all attributes showed great correct response rates, as happened to the results obtained using attributes combination.