Numerical methods for fMRI data analysis

Brain imaging data are increasingly analyzed via a range of machine-learning methods. In this thesis, we discuss three specific contributions to the field of neuroimaging analysis methods: 1. To apply a recently-developed technique for identifying and viewing similarity structure in neuroimaging data, in which candidate representational structures are ranked; 2. Provide side-by-side analyses of neuroimaging data by a typical non-hierarchical (SVM) versus hierarchical (Decision Tree) machine-learning classification methods; and 3. To develop a novel programming environment for PyMVPA, a current popular analysis toolbox, such that users will be able to type a small number of packaged commands to carry out a range of standard analyses. We carried out our analysis with an fMRI data set generated using auditory stimuli. Tree and Ring were the best voted structural representations we obtained by applying the Kemp\u27s algorithm. Machine-learning classification resulted in accuracy values that were similar for both decision tree and SVM algorithms. Coding for different sound categories primarily occurred in the temporal lobes of the brain. We discovered a few non-temporal regions of the brain coding for these auditory sounds as well

JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

Sensitivity of Semantic Signatures in Text Mining

The rapid development of the Internet and the ability to store data relatively inexpensively has contributed to an information explosion that did not exist a few years ago. Just a few keystrokes on search engines on any given subject will provide more web pages than any time before. As the amount of data available to us is so overwhelming, the ability to extract relevant information from it remains a challenge.;Since 80% of the available data stored world wide is text, we need advanced techniques to process this textual data and extract useful in formation. Text mining is one such process to address the information explosion problem that employs techniques such as natural language processing, information retrieval, machine learning algorithms and knowledge management. In text mining, the subjected text undergoes a transformation where essential attributes of the text are derived. The attributes that form interesting patterns are chosen and machine learning algorithms are used to find similar patterns in desired corpora. At the end, the resulting texts are evaluated and interpreted.;In this thesis we develop a new framework for the text mining process. An investigator chooses target content from training files, which is captured in semantic signatures. Semantic signatures characterize the target content derived from training files that we are looking for in testing files (whose content is unknown). The semantic signatures work as attributes to fetch and/or categorize the target content from a test corpus. A proof of concept software package, consisting of tools that aid an investigator in mining text data, is developed using Visual studio, C# and .NET framework.;Choosing keywords plays a major role in designing semantic signatures; careful selection of keywords leads to a more accurate analysis, especially in English, which is sensitive to semantics. It is interesting to note that when words appear in different contexts they carry a different meaning. We have incorporated stemming within the framework and its effectiveness is demonstrated using a large corpus. We have conducted experiments to demonstrate the sensitivity of semantic signatures to subtle content differences between closely related documents. These experiments show that the newly developed framework can identify subtle semantic differences substantially

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

A graph-based approach for the retrieval of multi-modality medical images

Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

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