Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.Dissertação (Mestrado)O câncer de pulmão é o mais comum de todos os tumores malignos, com 1,59 milhões de novos casos em todo o mundo no ano de 2012. A detecção precoce é o principal fator que determina a sobrevivência de pacientes acometidos por essa doença. Ainda, o estadiamento é importante para definir o método terapêutico mais adequado, bem como sugerir o prognóstico e a evolução clínica da doença. Dentre os exames utilizados para detecção de câncer pulmonar, a tomografia computadorizada têm sido o exame mais indicado. Porém, imagens de tomografia computadorizada são naturalmente complexas e médicos mesmo que experientes são sujeitos a falhas de detecção ou de classificação. No sentido de ajudar o processo de detecção de neoplasias, sistemas de auxílio ao diagnostico vem sendo desenvolvidos, o que pode ajudar a diminuir a quantidade de falsos positivos em biópsias. Neste trabalho foi desenvolvido um sistema de classificação automática de nódulos pulmonares em imagens de tomografia computadorizada utilizando Redes Neurais Artificias. Para isso, foram extraídos atributos morfológicos, de textura e de intensidade, de nódulos pulmonares que foram recortados de imagens tomográficas utilizando regiões de interesse elípticas e, posteriormente, segmentados pelo método de Otsu. Esses atributos foram selecionados por meio de testes estatísticos de comparação populacional (teste T de Student e teste U de Mann-Whitney) de onde originou um ranking. Os atributos, após a seleção, foram inseridos em redes neurais artificiais do tipo Backpropagation para compor dois tipos de classificação; uma para classificar se os nódulos são malignos ou benignos (rede 1); e outra para classificar dois tipos de lesões malignas (rede 2), formando, assim, um classificador em cascata. As melhores redes foram associadas e sua eficácia foi medida por meio da área sob a curva ROC, onde a rede 1 e a rede 2 obtiveram desempenho igual a 0,901 e 0,892 respectivamente

A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images

The minimal change disease (MCD) and glomerulosclerosis (GS) are two common kidney diseases. Unless adequately treated, these diseases leads to chronic kidney diseases. Accurate differentiation of these two diseases is of paramount importance as their methods of treatment and prognoses are different. Thus, this article propose a method capable of differentiating MCD from GS in glomerulus biopsies images based on a new hybrid deep and texture feature space. We conducted an extensive study to determine the best set of features for image representation. Our feature extraction methodology, which includes Haraliks and geostatistics texture descriptors and pre-trained CNNs, resulted in 13,476 characteristics. We then used mutual information to order the elements by importance and select the best set for differentiating MCD from GS using the random forest classifier. The proposed method achieved an accuracy of 90.3% and a Kappa index of 80.5%. Representation of glomerulus biopsy images with a hybrid of deep and textural features facilitates the accurate differentiation of GS and MCD

DETECÇÃO E DIAGNÓSTICO DE MASSAS EM MAMOGRAFIA: revisão bibliográfica

Resumo: O câncer de mama tem se tornado cada dia mais freqüente entre a população feminina acima dos 40 anos. Somente para o ano de 2011 são estimados, no Brasil, 49 mil novos casos. Uma das maneiras para detectar os tumores não palpáveis que causam câncer de mama é realizar uma radiografia (mamografia) das mamas. A  mamografia é atualmente a melhor técnica de detecção precoce de lesões não apalpáveis na mama com altas chances de ser um câncer curável. Sabe-se que as chances de cura do câncer de mama são, relativamente altas, se detectado nos estágios inicias. Entretanto, a sensibilidade desse exame pode variar bastante, em decorrência de fatores como qualidade do exame ou experiência do especialista. Dessa forma, a utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de uma detecção e diagnósticos corretos, ou seja, uma segunda opinião, auxiliando os especialistas na tomada de decisões em um tratamento do câncer de mama. Este artigo faz uma revisão bibliográfica de trabalhos voltados para detecção e diagnóstico de massas.Palavras-chave: Massa. Mamografia. Detecção. Diagnóstico. Câncer de mama.MAMMOGRAPHY MASS DETECTION AND DIAGNOSIS: a surveyAbstract: Breast cancer has become increasingly common among the female population over 40 years old. Only for the year 2011 are estimated, in Brazil, 49 000 new cases. One way to detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. Mammography is currently the best technique for early detection of non-palpable breast lesions with high chances of being a curable cancer. It is known that the chances of a cure for breast cancer are relatively high if detected in early stages. However, the sensitivity of this exam can vary greatly due to factors such as quality of examination or experience of the specialist. Thus, the use of CAD systems and CADX has contributed to increase the chances of detection and correct diagnosis, working as a second opinion in treatment of breast cancer. This article is a literature review of studies focused on detection and diagnosis of masses.Keywords: Mass. Mammography. Detection. Diagnosis. Breast cancer.DETECCIÓN Y DIAGNÓSTICO DE MASAS EN UNA MAMOGRAFÍA: una revisión de la literatura Resumen: El cáncer de mama se ha tornado cada vez más común entre la población femenina de más de 40 años. Sólo para el año 2011 se estima que en Brasil habrán 49 000 nuevos casos. Una forma de detectar tumores no palpables que causan el cáncer de mama es realizar una radiografía (mamografía) de los senos. La mamografía es actualmente la mejor técnica para la detección precoz de lesiones mamarias no palpables, con altas posibilidades de ser un cáncer curable. Se sabe que las posibilidades de una cura para el cáncer de mama son relativamente altas si se detecta en etapas tempranas. Sin embargo, la sensibilidad de esta prueba pueden variar considerablemente debido a factores como la calidad de los exámenes o la experiencia del especialista. Por lo tanto, el uso de sistemas CAD y CADX ha contribuido a aumentar las posibilidades de  detección y el diagnóstico correcto, o una segunda opinión, ayudando a los expertos en la tomada de decisiones en el tratamiento del cáncer de mama. Este artículo es una revisión de la literatura de trabajos sobre detección y diagnóstico de masas.Palabras clave: Masa. Mamografía. Detección. Diagnóstico de cáncer de mama

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Computerized cancer malignancy grading of fine needle aspirates

According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac

Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

Agroecological Approaches for Soil Health and Water Management

In the last century, innovations in agricultural technologies centered on maximizing food production to feed the growing population have contributed to significant changes in agroecosystem processes, including carbon, nutrients, and water cycling. There are growing concerns regarding soil fertility depletion, soil carbon loss, greenhouse gas emissions, irrigational water scarcity, and water pollution, affecting soil health, agricultural productivity, systems sustainability, and environmental quality. Soils provide the foundation for food production, soil water and nutrient cycling, and soil biological activities. Therefore, an improved understanding of biochemical pathways of soil organic matter and nutrient cycling, microbial community involved in regulating soil health, and soil processes associated with water flow and retention in soil profile helps design better agricultural systems and ultimately support plant growth and productivity. This book, Agroecological Approaches in Soil and Water Management, presents a collection of original research and review papers studying physical, chemical, and biological processes in soils and discusses multiple ecosystem services, including carbon sequestration, nutrients and water cycling, greenhouse gas emissions, and agro-environmental sustainability. We covered tillage, nutrients, irrigation, amendments, crop rotations, crop residue management practices for improving soil health, soil C and nutrient cycling, greenhouse gas emissions, soil water dynamics, and hydrological processes