1,291 research outputs found

    Hierarchical Fusion Based Deep Learning Framework for Lung Nodule Classification

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    Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes the raw images. The second model uses a single type feature images having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. In addition, this dissertation proposes a new Trilateral Filter to extract salient content of 2D images. This new filter includes a second anisotropic Laplacian kernel in addition to the Bilateral filter’s range kernel. The proposed CAD frameworks are tested using lung CT scans from the LIDC/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification

    Bi-Directional SIFT Predicts a Subset of Activating Mutations

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    Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer

    Next Generation of Product Search and Discovery

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    Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics, and CD/DVDs, online shopping has surpassed or even replaced the traditional shopping method. Compared with traditional retailing, e-commerce is information intensive. One of the key factors to succeed in e-business is how to facilitate the consumers’ approaches to discover a product. Conventionally a product search engine based on a keyword search or category browser is provided to help users find the product information they need. The general goal of a product search system is to enable users to quickly locate information of interest and to minimize users’ efforts in search and navigation. In this process human factors play a significant role. Finding product information could be a tricky task and may require an intelligent use of search engines, and a non-trivial navigation of multilayer categories. Searching for useful product information can be frustrating for many users, especially those inexperienced users. This dissertation focuses on developing a new visual product search system that effectively extracts the properties of unstructured products, and presents the possible items of attraction to users so that the users can quickly locate the ones they would be most likely interested in. We designed and developed a feature extraction algorithm that retains product color and local pattern features, and the experimental evaluation on the benchmark dataset demonstrated that it is robust against common geometric and photometric visual distortions. Besides, instead of ignoring product text information, we investigated and developed a ranking model learned via a unified probabilistic hypergraph that is capable of capturing correlations among product visual content and textual content. Moreover, we proposed and designed a fuzzy hierarchical co-clustering algorithm for the collaborative filtering product recommendation. Via this method, users can be automatically grouped into different interest communities based on their behaviors. Then, a customized recommendation can be performed according to these implicitly detected relations. In summary, the developed search system performs much better in a visual unstructured product search when compared with state-of-art approaches. With the comprehensive ranking scheme and the collaborative filtering recommendation module, the user’s overhead in locating the information of value is reduced, and the user’s experience of seeking for useful product information is optimized

    RGB-D And Thermal Sensor Fusion: A Systematic Literature Review

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    In the last decade, the computer vision field has seen significant progress in multimodal data fusion and learning, where multiple sensors, including depth, infrared, and visual, are used to capture the environment across diverse spectral ranges. Despite these advancements, there has been no systematic and comprehensive evaluation of fusing RGB-D and thermal modalities to date. While autonomous driving using LiDAR, radar, RGB, and other sensors has garnered substantial research interest, along with the fusion of RGB and depth modalities, the integration of thermal cameras and, specifically, the fusion of RGB-D and thermal data, has received comparatively less attention. This might be partly due to the limited number of publicly available datasets for such applications. This paper provides a comprehensive review of both, state-of-the-art and traditional methods used in fusing RGB-D and thermal camera data for various applications, such as site inspection, human tracking, fault detection, and others. The reviewed literature has been categorised into technical areas, such as 3D reconstruction, segmentation, object detection, available datasets, and other related topics. Following a brief introduction and an overview of the methodology, the study delves into calibration and registration techniques, then examines thermal visualisation and 3D reconstruction, before discussing the application of classic feature-based techniques as well as modern deep learning approaches. The paper concludes with a discourse on current limitations and potential future research directions. It is hoped that this survey will serve as a valuable reference for researchers looking to familiarise themselves with the latest advancements and contribute to the RGB-DT research field.Comment: 33 pages, 20 figure

    Instrumentation and development of a mass spectrometry system for the study of gas-phase biomolecular ion reactions

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    Gas-phase reactions of biomolecular ions are highly relevant to the understanding of structures and functions of the biomolecules. Mass spectrometry is a powerful tool in investigating gas-phase ion chemistry. Various mass spectrometers have been developed to explore ion/molecule reactions, ion/ion reactions, ion/photon reactions, ion/radical reactions etc., both at atmospheric pressure and in vacuum. In-vacuum reactions have an advantage of involving pre-selecting the ions for the reactions using a mass analyzer. Over the decades, a variety of mass analyzers have been employed in the research of ion chemistry. Hybrid configurations, such as quadrupole ion trap with a time-of-flight and or a quadrupole ion trap tandem with an Orbitrap, have been utilized to improve the performances for both the reaction (in trapping mode) and the mass analysis (accurate mass measurements). Complicated instrument structures, including ion optics, multiple mass analyzers and differential pumping for high vacuum, are typically required for the mass spectrometers for gas phase ion chemistry study. An alternative approach is to simplify the instrumentation by using pulsed discontinuous atmospheric pressure interfaces for introducing ionic or neutral reactants and a single ion trap as both the reactor and the mass analyzer. Such a simple mass spectrometry system was set up and demonstrated using two discontinuous atmospheric pressure interfaces in the study for this thesis. It was capable of carrying out ion/molecule and ion/ion reactions at an elevated pressure without the needs of ion optics or differential pumping system. Together with a pyrolysis radical source, in-vacuum ion/radical reactions were performed and their associated chemistry was studied. Radicals are important intermediates related to biochemical processes and biological functions. There are very limited techniques to monitor the reactive intermediates in-situ during a multi-step reaction in aqueous phase. On the other hand, these intermediates can be cooled down and preserved into a single-step procedure in gas-phase reactions since they only occur via collisions. Therefore, the fundamental study of gas-phase radical ion chemistry will provide evidences of the reactivity, energetics, and structural information of biological radicals, which has the potential to solve puzzles of aging, disease biomarker identification, and enzymatic activities. Using the system described above, a new reaction between protonated alkyl amines and pyrolysis formed cyclopropenylidene carbene was discovered, as the first experimental evidence of the reactivity of cyclopropenylidene. Given the important role of cyclopropenylidene in the combustion chemistry, organic synthesis, and interstellar chemistry, it is highly desirable to establish a fundamental understanding of their physical and chemical properties. The amine/cyclopropenylidene reactions were systematically studied using both theoretical calculation and experimental evidences. A proton-bound dimer reaction mechanism was proposed, with the amine and the carbene sharing a proton to form a complex as the first step, which was closely related to the high gas-phase basicity of cyclopropenylidene. Subsequent unimolecular dissociation of the complex yielded three possible reaction pathways, including proton-transfer to the carbene, covalent product formation, and direct separation. These reactions were studied with a variety of alkyl amines of different gas-phase basicities. For the covalent complex formation, partial protonation on cyclopropenylidene within the dimer facilitates subsequent nucleophilic attack to the carbene carbon by the amine nitrogen and leads to a C-N bond formation. The highest yield of covalent complex was achieved with the gas-phase basicity of the amine slightly lower but comparable to cyclopropenylidene. The results demonstrated a new reaction pathway of cyclopropenylidene besides the formation of cyclopropenium, which has long been considered as a dead end in interstellar carbon chemistry. Further reactivity study of cyclopropenylidene towards biomolecular ions was also carried out for nucleobases, nucleosides, amino acids, peptides, proteins, and lipids. The reaction to form proton-bound dimer for protonated biomolecular ions remained as the dominant reaction pathway. Interestingly, other possible reaction pathways, such as modifications of thiyl group or disulfide bonds, double bond addition, and single bond insertion, were inhibited in gas-phase ion/carbene reactions. Such results inferred that the reactivity of neutral species was not directly applicable to ion reactions, with the proton involved in the gas-phase biomolecular ion reactions

    Pattern Recognition

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    Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
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