XPySom: High-performance self-organizing maps

In this paper, we introduce XPySom, a new open-source Python implementation of the well-known Self-Organizing Maps (SOM) technique. It is designed to achieve high performance on a single node, exploiting widely available Python libraries for vector processing on multi-core CPUs and GP-GPUs. We present results from an extensive experimental evaluation of XPySom in comparison to widely used open-source SOM implementations, showing that it outperforms the other available alternatives. Indeed, our experimentation carried out using the Extended MNIST open data set shows a speed-up of about 7x and 100x when compared to the best open-source multi-core implementations we could find with multi-core and GP-GPU acceleration, respectively, achieving the same accuracy levels in terms of quantization error

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

Development of medical image/video segmentation via deep learning models

Image segmentation has a critical role in medical diagnosis systems as it is mostly the initial stage, and any error would be propagated in the subsequent analysis. Certain challenges, including Irregular border, low quality of images, small Region of Interest (RoI) and complex structures such as overlapping cells in images impede the improvement of medical image analysis. Deep learning-based algorithms have recently brought superior achievements in computer vision. However, there are limitations to their application in the medical domain including data scarcity, and lack of pretrained models on medical data. This research addresses the issues that hinder the progress of deep learning methods on medical data. Firstly, the effectiveness of transfer learning from a pretrained model with dissimilar data is investigated. The model is improved by integrating feature maps from the frequency domain into the spatial feature maps of Convolutional Neural Network (CNN). Training from scratch and the challenges ahead were explored as well. The proposed model shows higher performance compared to state-of-the-art methods by %2:2 and %17 in Jaccard index for tasks of lesion segmentation and dermoscopic feature segmentation respectively. Furthermore, the proposed model benefits from significant improvement for noisy images without preprocessing stage. Early stopping and drop out layers were considered to tackle the overfitting and network hyper-parameters such as different learning rate, weight initialization, kernel size, stride and normalization techniques were investigated to enhance learning performance. In order to expand the research into video segmentation, specifically left ventricular segmentation, U-net deep architecture was modified. The small RoI and confusion between overlapped organs are big challenges in MRI segmentation. The consistent motion of LV and the continuity of neighbor frames are important features that were used in the proposed architecture. High level features including optical flow and contourlet were used to add temporal information and the RoI module to the Unet model. The proposed model surpassed the results of original Unet model for LV segmentation by a %7 increment in Jaccard index