A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Malaria refers to a contagious mosquito-borne disease caused by parasite genus plasmodium transmitted by mosquito female Anopheles. As infected mosquito bites a person, the parasite multiplies in the host's liver and start destroying the red-cells. The disease is examined visually under the microscope for infected red-cells. This diagnosis depends upon the expertise and experience of pathologists and reports may vary in different laboratories doing a manual examination. Another way around, many machine learning techniques have been applied for spontaneous detection of blood smears. However, feature engineering is a challenging task that requires expertise to adjust positional and morphological features. Therefore, this study proposes a novel Stacked Convolutional Neural Network architecture that improves the automatic detection of malaria without considering the hand-crafted features. The 5-fold cross-validation process on 27, 558 cell images with equal instances of parasitized and uninfected cells on a publicly available dataset from the National Institute of health, the accuracy of our proposed model is 99.98%. Furthermore, the statistical results revealed that the proposed model is superior to the state-of-the-art models with 100% precision, 99.9% recall, and 99% f1-measure

Comparação de arquiteturas de Deep Learning para segmentação de imagens dermatoscópicas de melanoma

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, Engenharia Eletrônica, 2020.No Brasil, o câncer de pele representa cerca de 33% dos diagnósticos dentre os tipos de câncer, sendo apenas 3% causados pelo melanoma. Entretanto, esse tipo de câncer possui a maior taxa de mortalidade dentre os cânceres de pele, cerca de 7%. Por tratar-se de uma doença com alta taxa de mortalidade, o diagnóstico precoce do melanoma em seu estágio inicial é essencial para um prognóstico positivo da doença. Devido aos avanços tecnológicos, novos métodos de diagnósticos de doenças de pele estão sendo desenvolvidos para auxiliar profissionais médicos, como por exemplo o diagnóstico auxiliado por computador que utiliza técnicas de aprendizado de máquina e suas ramificações. A segmentação é um dos passos mais importantes do diagnóstico auxiliado por computador, pois acaba afetando a precisão das etapas seguintes. Este trabalho tem como objetivo comparar diferentes técnicas de segmentação de imagens baseadas em aprendizado profundo para segmentação de melanoma em imagens dermatoscópicas. Os backbones DenseNet-121, Resnet-50 e VGG-19 foram utilizados na etapa de encoder da U-Net para a realização do processo de segmentação. As arquiteturas foram treinadas e testadas utilizando o dataset ISIC 2017 com e sem a utilização da técnica de aumento de dados a fim de avaliar o impacto desta técnica nas métricas obtidas. Posteriormente, após a obtenção do modelo treinado, o mesmo foi testado no dataset PH2 . Todo o processo de implementação deste trabalho foi feito no ambiente computacional Google Colab em sua versão Pro, utilizando o TensorFlow e o Keras como as principais bibliotecas na implementação das arquiteturas. A arquitetura U-Net + ResNet-50 apresentou índice Jaccard de 81.94%, melhor índice nas médias obtidas no dataset ISIC com a utilização do aumento de dados, porém, o melhor modelo obtido foi apresentado pela arquitetura DenseNet-121 com 83.64% utilizando o dataset \u1d43c\u1d446\u1d43c\u1d436\u1d434 com aumento de dados.In Brazil, skin cancer represents about 33% of diagnoses among types of cancer, with only 3% caused by melanoma, however, this type of cancer have a higher mortality rate among skin cancers, about 7%. As it is a disease with a high mortality rate, the early diagnosis of melanoma in its initial stage is essential for a positive prognosis of the disease. Due to technological advances, new methods of diagnosing skin diseases are being developed to assist medical professionals, such as the diagnosis aided by a computer using machine learning techniques and their ramifications. Segmentation is one of the most important steps of computer-aided diagnosis, as it ends up affecting the accuracy of the following steps. This work aims to compare different image segmentation techniques based on deep learning for melanoma segmentation in dermoscopic images. The DenseNet-121, Resnet-50 and VGG-19 backbones were used in the U-Net encoder stage to perform the segmentation process. The architectures were trained and tested using the ISIC 2017 dataset with and without the use of the data augmentation technique in order to assess the impact of this technique on the obtained metrics. Subsequently, after obtaining the trained model, it was tested on the PH2 dataset. The entire process of implementing this work was done in the Google Colab computing environment in its Pro version, using TensorFlow and Keras as the main libraries in the implementation of the architectures. The U-Net + ResNet-50 architecture presented a Jaccard index of 81.94%, the best index in the averages obtained in the ISIC dataset with the use of data increase, however the best model obtained was presented by the DenseNet-121 architecture with 83.64% using the \u1d43c\u1d446\u1d43c\u1d436\u1d434 dataset with data augmentation

Low-Overhead Techniques For Secure And Reliable Gpu Computing

In recent years, Graphics Processing Units (GPUs) have become a de facto choice to accelerate the computations in various domains such as machine learning, security, financial and scientific computing. GPUs leverage the inherent data parallelism in the target applications to provide high throughput at superior energy efficiency. Due to the rising usage of GPUs for a large number of applications, they are facing new challenges, especially in the security and reliability domains. From the security side, recently several microarchitectural attacks targeting GPUs have been demonstrated. These attacks leak the secret information stored on GPUs, for example, the parameters of a neural network (NN) model and the private user information. From the reliability side, the innovations to improve GPU memory systems are making them more susceptible to errors. My dissertation research focuses on addressing these security and reliability challenges in GPUs while minimizing the associated overhead of the proposed protection mechanisms. To improve GPU security, we focus on the previously demonstrated correlation timing attack. Such an attack exploits the deterministic nature of the coalescing mechanism in GPUs to correlate the execution time and the number of accesses. Consequently, an attacker can recover the encryption keys stored on GPUs. Therefore, to counter the correlation timing attack, we first introduce a randomized coalescing defense scheme (RCoal). RCoal randomizes the coalescing logic such that the attacker fails to correlate the execution time and the number of accesses. As a result, RCoal thwarts the correlation timing attack. Next, we propose a bucketing-based coalescing defense scheme, BCoal, which minimizes the variation in the number of memory accesses by generating a predetermined number (called buckets) of memory accesses. With low variation in the number of memory accesses, the attacker cannot correlate the application execution time and the secret information, thus failing the correlation timing attack. BCoal generates less memory traffic than RCoal and, therefore, is performance efficient. To improve GPU reliability, we address the data memory faults in GPU caches and DRAM. Existing reliability mechanisms of redundancy and check-pointing fail to scale with the increasing memory/computational demands on GPUs and quickly become impractical. To address this problem, we study a wide range of applications to nd that a very small fraction of the data memory is most vulnerable to faults. This small fraction of the data is not only highly accessed but also highly shared across GPU threads. Consequently, we propose and develop two reliability schemes to detect-only and to detect/correct faults in this most vulnerable data while incurring low overhead. The focus of ongoing and future work is to improve the reliability of machine learning applications

Sparse fully convolutional network for face labeling

© 2018 Elsevier B.V. This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections. To ease this problem, group Lasso regularization and intra-group Lasso regularization are utilized to sparsify the convolutional layers of the FCN. Based on this framework, parameters that correspond to the same output channel are grouped into one group, and these parameters are simultaneously zeroed out during training. For the parameters in groups that are not zeroed out, intra-group Lasso provides further regularization. The essence of the regularization framework lies in its ability to offer better feature selection and higher sparsity. Moreover, a fully connected conditional random fields (CRF) model is used to refine the output of the sparse FCN. The proposed approach is evaluated on the LFW face dataset with the state-of-the-art performance. Compared with a non-regularized FCN, the sparse FCN reduces the number of parameters by 91.55% while increasing the segmentation performance by 11% relative error reduction