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arXiv 2022年

作者： Nan, Yang Del Ser, Javier Tang, Zeyu Tang, Peng Xing, Xiaodan Fang, Yingying Herrera, Francisco Pedrycz, Witold Walsh, Simon Yang, Guang National Heart and Lung Institute Imperial College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao Spain Derio Spain Department of Informatics Technical University of Munich Germany University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abgudulaziz University Jeddah Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw Poland Royal Brompton Hospital Sydney Street London United Kingdom

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis. © 2022, CC BY-NC-ND.

关键词： COVID-19

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Towards the design of a metrics cataloging system by exploiting conceptual and semantic web approaches

Towards the design of a metrics cataloging system by exploit...

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International Conference on Web Engineering, ICWE 2003

作者： Olsina, L. Martin, Ma A. Fons, J. Abrahao, S. Pastor, O. GIDIS Department of Informatics Engineering School at UNLPam Calle 9 y 110 General PicoLa Pampa6360 Argentina Department of Informatics Systems and Computing at UPV Camino de la Vera s/n. Aptd. 22.012 ValenciaE-46071 Spain

ISBN: (纸本)9783540450689

In this article we thoroughly discuss conceptual and navigational modeling and query issues for repositories of metrics and their cataloging system by exploiting as well the power of the semantic web approach. This environment can finally allow tools, evaluators and other stakeholders to have service and consultation mechanisms starting from a sound specification of the entity type, the attribute definition and motivation, the metric definition as well as the measure type, unit, scale type, data collection instruments, protocols, among other metadata. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Semantic Web

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