Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks.

Due to wearables' popularity, human activity recognition (HAR) plays a significant role in people's routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models' decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR's high performance with SD comes not only from physical activity learning but also from learning an individual's signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.

Evaluation of autofocus functions of conventional sputum smear microscopy for tuberculosis.

This article presents a systematic analysis of focus functions in conventional sputum smear microscopy for tuberculosis. This is the first step in the development of automatic microscopy. Nine autofocus functions are analyzed in a set of 1200 images with varying degrees of content density. These functions were evaluated using quantitative procedures. The main accomplishment of this work was to show that an autofocus function based on variance measures produced the best results for tuberculosis images.

Automatic identification of mycobacterium tuberculosis with conventional light microscopy.

This article presents an automatic identification method of mycobacterium tuberculosis with conventional microscopy images based on Red and Green color channels using global adaptive threshold segmentation. Differing from fluorescence microscopy, in the conventional microscopy the bacilli are not easily distinguished from the background. The key to the bacilli segmentation method employed in this work is the use of Red minus Green (R-G) images from RGB color format. In this image, the bacilli appear as white regions on a dark background. Some artifacts are present in the (R-G) segmented image. To remove them we used morphological, color and size filters. The best sensitivity achieved was about 76.65%. The main contribution of this work was the proposal of the first automatic identification method of tuberculosis bacilli for conventional light microscopy.

Revisão bibliográfica sobre o uso da cintilografia mamária com metoxi-isobutil-isonitrila-tecnécio 99m (MIBI-Tc99m) no diagnóstico diferencial de câncer de mama / Literature review on the use of scintimamography with Technetium-99m methoxyisobutyl-isonitrile (99mTc-MIBI) for the differencial diagnosis of breast carcinoma

A mamografia por Raios X tem apresentado alta sensibilidade na detecção de lesões de mama, porém a mesma tem se mostrado limitada na tarefa de diagnóstico diferencial. Muitos especialistas em câncer de mama crêem na necessidade de uma ferramenta de diagnóstico por imagem mais específica, uma vez que, da estimativa de 700.000 biópsias de mama feitas anualmente nos Estados Unidos, somente 1 em 4 apresenta achado de câncer. Outras técnicas de imagens estão sendo pesquisadas e, pelos resultados apresentados, poderão, em breve, assumir papel importante na detecção de lesões de mama em pacientes assintomáticos. Dentre essas novas técnicas destacamos a cintilografia mamária marcada com MIBI-99m Tc. Neste trabalho, são descritos os aspectos técnicos desse novo exame, realizado segundo a técnica proposta por Khalkhali e são apresentados os resultados de vários estudos publicados nos últimos anos.