Chagas Parasite Detection in Blood Images Using AdaBoost

The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods

Sistema de reconhecimento de expressões faciais para deteção de stress

Stress is the body's natural reaction to external and internal stimuli. Despite being something natural, prolonged exposure to stressors can contribute to serious health problems. These reactions are reflected not only physiologically, but also psychologically, translating into emotions and facial expressions. Once this relationship between the experience of stressful situations and the demonstration of certain emotions in response was understood, it was decided to develop a system capable of classifying facial expressions and thereby creating a stress detector. The proposed solution consists of two main blocks. A convolutional neural network capable of classifying facial expressions, and an application that uses this model to classify real-time images of the user's face and thereby verify whether or not it shows signs of stress. The application consists in capturing real-time images from the webcam, extract the user's face, classify which facial expression he expresses, and with these classifications assess whether or not he shows signs of stress in a given time interval. As soon as the application determines the presence of signs of stress, it notifies the user. For the creation of the classification model, was used transfer learning, together with finetuning. In this way, we took advantage of the pre-trained networks VGG16, VGG19, and Inception-ResNet V2 to solve the problem at hand. For the transfer learning process, were also tried two classifier architectures. After several experiments, it was determined that VGG16, together with a classifier made up of a convolutional layer, was the candidate with the best performance at classifying stressful emotions. Having presented an MCC of 0.8969 in the test images of the KDEF dataset, 0.5551 in the Net Images dataset, and 0.4250 in the CK +.O stress é uma reação natural do corpo a estímulos externos e internos. Apesar de ser algo natural, a exposição prolongada a stressors pode contribuir para sérios problemas de saúde. Essas reações refletem-se não só fisiologicamente, mas também psicologicamente. Traduzindose em emoções e expressões faciais. Uma vez compreendida esta relação entre a experiência de situações stressantes e a demonstração de determinadas emoções como resposta, decidiu-se desenvolver um sistema capaz de classificar expressões faciais e com isso criar um detetor de stress. A solução proposta é constituida por dois blocos fundamentais. Uma rede neuronal convolucional capaz de classificar expressões faciais e uma aplicação que utiliza esse modelo para classificar imagens em tempo real do rosto do utilizador e assim averiguar se este apresenta ou não sinais de stress. A aplicação consiste em captar imagens em tempo real a partir da webcam, extrair o rosto do utilizador, classificar qual a expressão facial que este manifesta, e com essas classificações avaliar se num determinado intervalo temporal este apresenta ou não sinais de stress. Assim que a aplicação determine a presença de sinais de stress, esta irá notificar o utilizador. Para a criação do modelo de classificação, foi utilizado transfer learning, juntamente com finetuning. Desta forma tirou-se partido das redes pre-treinadas VGG16, VGG19, e InceptionResNet V2 para a resolução do problema em mãos. Para o processo de transfer learning foram também experimentadas duas arquiteturas de classificadores. Após várias experiências, determinou-se que a VGG16, juntamente com um classificador constituido por uma camada convolucional era a candidata com melhor desempenho a classificar emoções stressantes. Tendo apresentado um MCC de 0,8969 nas imagens de teste do conjunto de dados KDEF, 0,5551 no conjunto de dados Net Images, e 0,4250 no CK+

Gesture tracking and neural activity segmentation in head-fixed behaving mice by deep learning methods

The typical approach used by neuroscientists is to study the response of laboratory animals to a stimulus while recording their neural activity at the same time. With the advent of calcium imaging technology, researchers can now study neural activity at sub-cellular resolutions in vivo. Similarly, recording the behaviour of laboratory animals is also becoming more affordable. Although it is now easier to record behavioural and neural data, this data comes with its own set of challenges. The biggest challenge, given the sheer volume of the data, is annotation. A traditional approach is to annotate the data manually, frame by frame. With behavioural data, manual annotation is done by looking at each frame and tracing the animals; with neural data, this is carried out by a trained neuroscientist. In this research, we propose automated tools based on deep learning that can aid in the processing of behavioural and neural data. These tools will help neuroscientists annotate and analyse the data they acquire in an automated and reliable way.La configuración típica empleada por los neurocientíficos consiste en estudiar la respuesta de los animales de laboratorio a un estímulo y registrar al mismo tiempo su actividad neuronal. Con la llegada de la tecnología de imágenes del calcio, los investigadores pueden ahora estudiar la actividad neuronal a resoluciones subcelulares in vivo. Del mismo modo, el registro del comportamiento de los animales de laboratorio también se está volviendo más asequible. Aunque ahora es más fácil registrar los datos del comportamiento y los datos neuronales, estos datos ofrecen su propio conjunto de desafíos. El mayor desafío es la anotación de los datos debido a su gran volumen. Un enfoque tradicional es anotar los datos manualmente, fotograma a fotograma. En el caso de los datos sobre el comportamiento, la anotación manual se hace mirando cada fotograma y rastreando los animales, mientras que, para los datos neuronales, la anotación la hace un neurocientífico capacitado. En esta investigación, proponemos herramientas automatizadas basadas en el aprendizaje profundo que pueden ayudar a procesar los datos de comportamiento y los datos neuronales.La configuració típica emprada pels neurocientífics consisteix a estudiar la resposta dels animals de laboratori a un estímul i registrar al mateix temps la seva activitat neuronal. Amb l'arribada de la tecnologia d'imatges basades en calci, els investigadors poden ara estudiar l'activitat neuronal a resolucions subcel·lulars in vivo. De la mateixa manera, el registre del comportament dels animals de laboratori també ha esdevingut molt més assequible. Tot i que ara és més fàcil registrar les dades del comportament i les dades neuronals, aquestes dades ofereixen el seu propi conjunt de reptes. El major desafiament és l'anotació de les dades, degut al seu gran volum. Un enfocament tradicional és anotar les dades manualment, fotograma a fotograma. En el cas de les dades sobre el comportament, l'anotació manual es fa mirant cada fotograma i rastrejant els animals, mentre que per a les dades neuronals, l'anotació la fa un neurocientífic capacitat. En aquesta investigació, proposem eines automatitzades basades en laprenentatge profund que poden ajudar a modelar les dades de comportament i les dades neuronals

Robust learning and segmentation for secure understanding

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 85-91).This thesis demonstrates methods useful in learning to understand images from only a few examples, but they are by no means limited to this application. Boosting techniques are popular because they learn effective classification functions and identify the most relevant features at the same time. However, in general, they overfit and perform poorly on data sets that contain many features, but few examples. A novel stochastic regularization technique is presented, based on enhancing data sets with corrupted copies of the examples to produce a more robust classifier. This regularization technique enables the gentle boosting algorithm to work well with only a few examples. It is tested on a variety of data sets from various domains, including object recognition and bioinformatics, with convincing results. In the second part of this work, a novel technique for extracting texture edges is introduced, based on the combination of a patch-based approach, and non-param8tric tests of distributions. This technique can reliably detect texture edges using only local information, making it a useful preprocessing step prior to segmentation. Combined with a parametric deformable model, this technique provides smooth boundaries and globally salient structures.by Ian Stefan Martin.M.Eng

Expressions of psychological stress on Twitter: detection and characterisation

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Long-term psychological stress is a significant predictive factor for individual mental health and short-term stress is a useful indicator of an immediate problem. Traditional psychology studies have relied on surveys to understand reasons for stress in general and in specific contexts. The popularity and ubiquity of social media make it a potential data source for identifying and characterising aspects of stress. Previous studies of stress in social media have focused on users responding to stressful personal life events. Prior social media research has not explored expressions of stress in other important domains, however, including travel and politics. This thesis detects and analyses expressions of psychological stress in social media. So far, TensiStrength is the only existing lexicon for stress and relaxation scores in social media. Using a word-vector based word sense disambiguation method, the TensiStrength lexicon was modified to include the stress scores of the different senses of the same word. On a dataset of 1000 tweets containing ambiguous stress-related words, the accuracy of the modified TensiStrength increased by 4.3%. This thesis also finds and reports characteristics of a multiple-domain stress dataset of 12000 tweets, 3000 each for airlines, personal events, UK politics, and London traffic. A two-step method for identifying stressors in tweets was implemented. The first step used LDA topic modelling and k-means clustering to find a set of types of stressors (e.g., delay, accident). Second, three word-vector based methods - maximum-word similarity, context-vector similarity, and cluster-vector similarity - were used to detect the stressors in each tweet. The cluster vector similarity method was found to identify the stressors in tweets in all four domains better than machine learning classifiers, based on the performance metrics of accuracy, precision, recall, and f-measure. Swearing and sarcasm were also analysed in high-stress and no-stress datasets from the four domains using a Convolutional Neural Network and Multilayer Perceptron, respectively. The presence of swearing and sarcasm was higher in the high-stress tweets compared to no-stress tweets in all the domains. The stressors in each domain with higher percentages of swearing or sarcasm were identified. Furthermore, the distribution of the temporal classes (past, present, future, and atemporal) in high-stress tweets was found using an ensemble classifier. The distribution depended on the domain and the stressors. This study contributes a modified and improved lexicon for the identification of stress scores in social media texts. The two-step method to identify stressors follows a general framework that can be used for domains other than those which were studied. The presence of swearing, sarcasm, and the temporal classes of high-stress tweets belonging to different domains are found and compared to the findings from traditional psychology, for the first time. The algorithms and knowledge may be useful for travel, political, and personal life systems that need to identify stressful events in order to take appropriate action.European Union's Horizon 2020 research and innovation programme under grant agreement No 636160-2, the Optimum project (www.optimumproject.eu)