71 research outputs found

    Application of Multiprotocol Medical Imaging Communications and an Extended DICOM WADO Service in a Teleradiology Architecture

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    Multiprotocol medical imaging communication through the Internet is more flexible than the tight DICOM transfers. This paper introduces a modular multiprotocol teleradiology architecture that integrates DICOM and common Internet services (based on web, FTP, and E-mail) into a unique operational domain. The extended WADO service (a web extension of DICOM) and the other proposed services allow access to all levels of the DICOM information hierarchy as opposed to solely Object level. A lightweight client site is considered adequate, because the server site of the architecture provides clients with service interfaces through the web as well as invulnerable space for temporary storage, called as User Domains, so that users fulfill their applications' tasks. The proposed teleradiology architecture is pilot implemented using mainly Java-based technologies and is evaluated by engineers in collaboration with doctors. The new architecture ensures flexibility in access, user mobility, and enhanced data security

    Profile Management System in Ubiquitous Healthcare Cloud Computing Environment

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    A shift from the doctor-centric model to a patient-centric model is required to face the challenges of the healthcare sector. The vision of patient-centric model can be materialized integrating ubiquitous healthcare and the notion of personalization in services. Cloud computing can be the underlying technology for ubiquitous healthcare. The use of profiles enables the personalization in healthcare services and the use of profile management systems facilitates the deployment of these services. In this paper, we propose a profile management system in ubiquitous healthcare cloud computing environment. The proposed system exploits the cloud computing technology and the smart card technology to increase the efficiency and the quality of the provided healthcare services in the context of the patient-centric model. Furthermore, we propose generic healthcare profile structures corresponding to the main classes of the participating entities in a ubiquitous healthcare cloud computing environment

    Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization

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    Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as key components of AutoML frameworks. However, the NAS problem remains challenging due to the combinatorially large design space and the significant search time (at least 200 GPU-hours). In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints. We propose a novel differentiable NAS formulation, namely Single-Path NAS, that uses one single-path over-parameterized ConvNet to encode all architectural decisions based on shared convolutional kernel parameters, hence drastically decreasing the search overhead. Single-Path NAS achieves state-of-the-art top-1 ImageNet accuracy (75.62%), hence outperforming existing mobile NAS methods in similar latency settings (~80ms). In particular, we enhance the accuracy-runtime trade-off in differentiable NAS by treating the Squeeze-and-Excitation path as a fully searchable operation with our novel single-path encoding. Our method has an overall cost of only 8 epochs (24 TPU-hours), which is up to 5,000x faster compared to prior work. Moreover, we study how different NAS formulation choices affect the performance of the designed ConvNets. Furthermore, we exploit the efficiency of our method to answer an interesting question: instead of empirically tuning the hyperparameters of the NAS solver (as in prior work), can we automatically find the hyperparameter values that yield the desired accuracy-runtime trade-off? We open-source our entire codebase at: https://github.com/dstamoulis/single-path-nas.Comment: Detailed extension (journal) of the Single-Path NAS ECMLPKDD'19 paper (arXiv:1904.02877

    An exploration of ranking heuristics in mobile local search

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    Users increasingly rely on their mobile devices to search local entities, typically businesses, while on the go. Even though recent work has recognized that the ranking signals in mo-bile local search (e.g., distance and customer rating score of a business) are quite different from general Web search, they have mostly treated these signals as a black-box to ex-tract very basic features (e.g., raw distance values and rating scores) without going inside the signals to understand how exactly they affect the relevance of a business. However, as it has been demonstrated in the development of general information retrieval models, it is critical to explore the un-derlying behaviors/heuristics of a ranking signal to design more effective ranking features. In this paper, we follow a data-driven methodology to study the behavior of these ranking signals in mobile local search using a large-scale query log. Our analysis reveals interesting heuristics that can be used to guide the exploita-tion of different signals. For example, users often take the mean value of a signal (e.g., rating) from the business result list as a “pivot ” score, and tend to demonstrate different click behaviors on businesses with lower and higher signal values than the pivot; the clickrate of a business generally is sublinearly decreasing with its distance to the user, etc. Inspired by the understanding of these heuristics, we further propose different transformation methods to generate more effective ranking features. We quantify the improvement of the proposed new features using real mobile local search logs over a period of 14 months and show that the mean average precision can be improved by over 7%
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