13 research outputs found

    Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach

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    The mammography image eccentric area is the breast density percentage measurement. The technical challenge of quantification in radiology leads to misinterpretation in screening. Data feedback from society, institutional, and industry shows that quantification and segmentation frameworks have rapidly become the primary methodologies for structuring and interpreting mammogram digital images. Segmentation clustering algorithms have setbacks on overlapping clusters, proportion, and multidimensional scaling to map and leverage the data. In combination, mammogram quantification creates a long-standing focus area. The algorithm proposed must reduce complexity and target data points distributed in iterative, and boost cluster centroid merged into a single updating process to evade the large storage requirement. The mammogram database's initial test segment is critical for evaluating performance and determining the Area Under the Curve (AUC) to alias with medical policy. In addition, a new image clustering algorithm anticipates the need for largescale serial and parallel processing. There is no solution on the market, and it is necessary to implement communication protocols between devices. Exploiting and targeting utilization hardware tasks will further extend the prospect of improvement in the cluster. Benchmarking their resources and performance is required. Finally, the medical imperatives cluster was objectively validated using qualitative and quantitative inspection. The proposed method should overcome the technical challenges that radiologists face

    Epileptic seizure prediction using machine learning techniques

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    Epileptic seizures affect about 1% of the world’s population, thus making it the fourth most common neurological disease, this disease is considered a neurological disorder characterized by the abnormal activity of the brain. Part of the population suffering from this disease is unable to avail themselves of any treatment, as this treatment has no beneficial effect on the patient. One of the main concerns associated with this disease is the damage caused by uncontrollable seizures. This damage affects not only the patient himself but also the people around him. With this situation in mind, the goal of this thesis is, through methods of Machine Learning, to create an algorithm that can predict epileptic seizures before they occur. To predict these seizures, the electroencephalogram (EEG) will be employed, since it is the most commonly used method for diagnosing epilepsy. Of the total 23 channels available, only 8 will be used, due to their location. When a seizure occurs, besides the visible changes in the EEG signal, at the moment of the seizure, the alterations before and after the epileptic seizure are also noticeable. These stages have been named in the literature: • Preictal: the moment before the epileptic seizure; • Ictal: the moment of the seizure; • Postictal: the moment after the seizure; • Interictal: space of time between seizures. The goal of the predictive algorithm will be to classify the different classes and study different classification problems by using supervised learning techniques, more precisely a classifier. By performing this classification when indications are detected that a possible epileptic seizure will occur, the patient will then be warned so that he can prepare for the seizure.Crises epiléticas afetam cerca de 1% da população mundial, tornando-a assim a quarta doença neurológica mais comum. Esta é considerada uma doença caracterizada pela atividade anormal do cérebro. Parte da população que sofre desta condição não consegue recorrer a qualquer tratamento, pois este não apresenta qualquer efeito benéfico no paciente. Uma das principais preocupações associadas com este problema são os danos causados pelas convulsões imprevisíveis. Estes danos não afetam somente o próprio paciente, como também as pessoas que o rodeiam. Com esta situação em mente, o objetivo desta dissertação consiste em, através de métodos de Machine Learning, criar um algoritmo capaz de prever as crises epiléticas antes da sua ocorrência. Para proceder à previsão destas convulsões, será utilizado o eletroencefalograma (EEG), uma vez que é o método mais usado para o diagnóstico de epilepsia. Serão utilizados apenas 8 dos 23 canais disponíveis, devido à sua localização. Quando ocorre uma crise, além das alterações visíveis no sinal EEG, não só no momento da crise, são também notáveis alterações antes e após a convulsão. A estas fases a literatura nomeou: • Pre-ictal: momento anterior à crise epilética; • Ictal: momento da convulsão; • Pós-ictal: momento posterior à crise; • Interictal: espaço de tempo entre convulsões. O objetivo do algoritmo preditivo será fazer a classificação das diferentes classes e o estudo de diferentes problemas de classificação, através do uso de técnicas de machine learning, mais precisamente um classificador. Ao realizar esta classificação, quando forem detetados indícios de que uma possível crise epilética irá ocorrer, o paciente será então avisado, podendo assim preparar-se para esta

    Data-efficient deep representation learning

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    Current deep learning methods succeed in many data-intensive applications, but they are still not able to produce robust performance due to the lack of training samples. To investigate how to improve the performance of deep learning paradigms when training samples are limited, data-efficient deep representation learning (DDRL) is proposed in this study. DDRL as a sub area of representation learning mainly addresses the following problem: How can the performance of a deep learning method be maintained when the number of training samples is significantly reduced? This is vital for many applications where collecting data is highly costly, such as medical image analysis. Incorporating a certain kind of prior knowledge into the learning paradigm is key to achieving data efficiency. Deep learning as a sub-area of machine learning can be divided into three parts (locations) in its learning process, namely Data, Optimisation and Model. Integrating prior knowledge into these three locations is expected to bring data efficiency into a learning paradigm, which can dramatically increase the model performance under the condition of limited training data. In this thesis, we aim to develop novel deep learning methods for achieving data-efficient training, each of which integrates a certain kind of prior knowledge into three different locations respectively. We make the following contributions. First, we propose an iterative solution based on deep learning for medical image segmentation tasks, where dynamical systems are integrated into the segmentation labels in order to improve both performance and data efficiency. The proposed method not only shows a superior performance and better data efficiency compared to the state-of-the-art methods, but also has better interpretability and rotational invariance which are desired for medical imagining applications. Second, we propose a novel training framework which adaptively selects more informative samples for training during the optimization process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model. We show that the proposed framework outperforms a random sampling method, which demonstrates effectiveness of the proposed framework. Thirdly, we propose a deep neural network model which produces the segmentation maps in a coarse-to-fine manner. The proposed architecture is a sequence of computational blocks containing a number of convolutional layers in which each block provides its successive block with a coarser segmentation map as a reference. Such mechanisms enable us to train the network with limited training samples and produce more interpretable results.Open Acces

    Convolutional neural networks for face recognition and finger-vein biometric identification

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    The Convolutional Neural Network (CNN), a variant of the Multilayer Perceptron (MLP), has shown promise in solving complex recognition problems, particularly in visual pattern recognition. However, the classical LeNet-5 CNN model, which most solutions are based on, is highly compute-intensive. This CNN also suffers from long training time, due to the large number of layers that ranges from six to eight. In this research, a CNN model with a reduced complexity is proposed for application in face recognition and finger-vein biometric identification. A simpler architecture is obtained by fusing convolutional and subsampling layers into one layer, in conjunction with a partial connection scheme applied between the first two layers in the network. As a result, the total number of layers is reduced to four. The number of feature maps at each layer is optimized according to the type of image database being processed. Consequently, the numbers of network parameters (including neurons, trainable parameters and connections) are significantly reduced, essentially increasing the generalization ability of the network. The Stochastic Diagonal Levenberg-Marquadt (SDLM) backpropagation algorithm is modified and applied in the training of the proposed network. With this learning algorithm, the convergence rate is accelerated such that the proposed CNN converges within 15 epochs. For face recognition, the proposed CNN achieves recognition rates of 100.00% and 99.50% for AT&T and AR Purdue face databases respectively. Recognition time on the AT&T database is less than 0.003 seconds. These results outperform previous existing works. In addition, when compared with the other CNN-based face recognizer, the proposed CNN model has the least number of network parameters, hence better generalization ability. A training scheme is also proposed to recognize new categories without full CNN training. In this research, a novel CNN solution for the finger-vein biometric identification problem is also proposed. To the best of knowledge, there is no previous work reported in literature that applied CNN for finger-vein recognition. The proposed method is efficient in that simple preprocessing algorithms are deployed. The CNN design is adapted on a finger-vein database, which is developed in-house and contains 81 subjects. A recognition accuracy of 99.38% is achieved, which is similar to the results of state-of-the-art work. In conclusion, the success of the research in solving face recognition and finger-vein biometric identification problems proves the feasibility of the proposed CNN model in any pattern recognition system

    A Systematic Evaluation of Methods to Separate X- and Y- Bearing Sperm

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    This project was initiated to determine if was possible to enrich either X- or Y- bearing sperm, and hence to preselect the sex of a child. Two of the possible reasons why couple might want to select the sex of a child are firstly because of a family history of an X-linked recessive genetic disorder, which usually only affect sons, and secondly families who have had several children of one sex. For this study, men with three or more children of the same sex were recruited following the publication of an article in The West Australian newspaper. The percentage of X- and Y- bearing sperm within the semen samples of men with three or more children of the same sex was determined using dual colour fluorescence in situ hybridisation (FISH). The aim of the investigation was to determine if these men had an altered ratio of X- to Y- bearing sperm, which would explain why these men had a predominance of children of one sex. Comprehensive analyses were also carried out on the semen samples. The reliability of the dual colour FISH technique was established using a number of standard metaphase spreads; from male and female subjects and an individual with Klinefelters syndrome. It was determined that dual colour FISH was a suitable technique for determining the percentage of X- or Y- bearing sperm within a sample. The semen samples were processed using one of two protocols. Samples from men with three or more daughters were treated using Human serum albumin columns, with the intention of increasing the percentage of Y-bearing sperm within the final fraction. It has been suggested that this method enriches the Y- bearing sperm from a sample due to the differential motility exhibited by the X- and Y- bearing sperm, although this characteristic has not been proven. Samples from men with three or more sons were processed using 8-layer ISolate® discontinuous gradients, with, the aim of enhancing the amount of X- bearing sperm within the final fraction. This method is based on the formation of the discontinuous gradients because Percoll has not been approved for the production of sperm fractions for human insemination. It has been suggested that the X- and Y- bearing sperm can be enriched using such gradients either as a result of differences in their velocity of sedimentation or due to a greater nett negative charge on the surface of X- bearing sperm. However, neither of these theories have been validated. As it has also been proposed that the survival rate of X- bearing sperm is slightly longer than that for Y- bearing sperm, this was also investigated. In summary no statistically significant enrichment of X- or Y- bearing sperm was observed following the treatment of the semen samples with either the ISolate® discontinuous gradient or the Human serum albumin column protocols. Nor was there any enrichment in X- bearing sperm due to their suggested greater survival time

    Contribuciones de las técnicas machine learning a la cardiología. Predicción de reestenosis tras implante de stent coronario

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    [ES]Antecedentes: Existen pocos temas de actualidad equiparables a la posibilidad de la tecnología actual para desarrollar las mismas capacidades que el ser humano, incluso en medicina. Esta capacidad de simular los procesos de inteligencia humana por parte de máquinas o sistemas informáticos es lo que conocemos hoy en día como inteligencia artificial. Uno de los campos de la inteligencia artificial con mayor aplicación a día de hoy en medicina es el de la predicción, recomendación o diagnóstico, donde se aplican las técnicas machine learning. Asimismo, existe un creciente interés en las técnicas de medicina de precisión, donde las técnicas machine learning pueden ofrecer atención médica individualizada a cada paciente. El intervencionismo coronario percutáneo (ICP) con stent se ha convertido en una práctica habitual en la revascularización de los vasos coronarios con enfermedad aterosclerótica obstructiva significativa. El ICP es asimismo patrón oro de tratamiento en pacientes con infarto agudo de miocardio; reduciendo las tasas de muerte e isquemia recurrente en comparación con el tratamiento médico. El éxito a largo plazo del procedimiento está limitado por la reestenosis del stent, un proceso patológico que provoca un estrechamiento arterial recurrente en el sitio de la ICP. Identificar qué pacientes harán reestenosis es un desafío clínico importante; ya que puede manifestarse como un nuevo infarto agudo de miocardio o forzar una nueva resvascularización del vaso afectado, y que en casos de reestenosis recurrente representa un reto terapéutico. Objetivos: Después de realizar una revisión de las técnicas de inteligencia artificial aplicadas a la medicina y con mayor profundidad, de las técnicas machine learning aplicadas a la cardiología, el objetivo principal de esta tesis doctoral ha sido desarrollar un modelo machine learning para predecir la aparición de reestenosis en pacientes con infarto agudo de miocardio sometidos a ICP con implante de un stent. Asimismo, han sido objetivos secundarios comparar el modelo desarrollado con machine learning con los scores clásicos de riesgo de reestenosis utilizados hasta la fecha; y desarrollar un software que permita trasladar esta contribución a la práctica clínica diaria de forma sencilla. Para desarrollar un modelo fácilmente aplicable, realizamos nuestras predicciones sin variables adicionales a las obtenidas en la práctica rutinaria. Material: El conjunto de datos, obtenido del ensayo GRACIA-3, consistió en 263 pacientes con características demográficas, clínicas y angiográficas; 23 de ellos presentaron reestenosis a los 12 meses después de la implantación del stent. Todos los desarrollos llevados a cabo se han hecho en Python y se ha utilizado computación en la nube, en concreto AWS (Amazon Web Services). Metodología: Se ha utilizado una metodología para trabajar con conjuntos de datos pequeños y no balanceados, siendo importante el esquema de validación cruzada anidada utilizado, así como la utilización de las curvas PR (precision-recall, exhaustividad-sensibilidad), además de las curvas ROC, para la interpretación de los modelos. Se han entrenado los algoritmos más habituales en la literatura para elegir el que mejor comportamiento ha presentado. Resultados: El modelo con mejores resultados ha sido el desarrollado con un clasificador extremely randomized trees; que superó significativamente (0,77; área bajo la curva ROC a los tres scores clínicos clásicos; PRESTO-1 (0,58), PRESTO-2 (0,58) y TLR (0,62). Las curvas exhaustividad sensibilidad ofrecieron una imagen más precisa del rendimiento del modelo extremely randomized trees que muestra un algoritmo eficiente (0,96) para no reestenosis, con alta exhaustividad y alta sensibilidad. Para un umbral considerado óptimo, de 1,000 pacientes sometidos a implante de stent, nuestro modelo machine learning predeciría correctamente 181 (18%) más casos en comparación con el mejor score de riesgo clásico (TLR). Las variables más importantes clasificadas según su contribución a las predicciones fueron diabetes, enfermedad coronaria en 2 ó más vasos, flujo TIMI post-ICP, plaquetas anormales, trombo post-ICP y colesterol anormal. Finalmente, se ha desarrollado una calculadora para trasladar el modelo a la práctica clínica. La calculadora permite estimar el riesgo individual de cada paciente y situarlo en una zona de riesgo, facilitando la toma de decisión al médico en cuanto al seguimiento adecuado para el mismo. Conclusiones: Aplicado inmediatamente después de la implantación del stent, un modelo machine learning diferencia mejor a aquellos pacientes que presentarán o no reestenosis respecto a los discriminadores clásicos actuales
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