Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

GraphR: Accelerating Graph Processing Using ReRAM

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.Comment: Accepted to HPCA 201

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively

Mediated data integration and transformation for web service-based software architectures

Service-oriented architecture using XML-based web services has been widely accepted by many organisations as the standard infrastructure to integrate heterogeneous and autonomous data sources. As a result, many Web service providers are built up on top of the data sources to share the data by supporting provided and required interfaces and methods of data access in a unified manner. In the context of data integration, problems arise when Web services are assembled to deliver an integrated view of data, adaptable to the specific needs of individual clients and providers. Traditional approaches of data integration and transformation are not suitable to automate the construction of connectors dedicated to connect selected Web services to render integrated and tailored views of data. We propose a declarative approach that addresses the oftenneglected data integration and adaptivity aspects of serviceoriented architecture