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Automated Retrieval of Focal Liver Lesions in Multi-phase CT Images Using Tensor Sparse Representation 9th

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Automated Retrieval of Focal Liver Lesions in Multi-phase CT...

9th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2021

作者： Wang, Jian Zhao, Junlin Han, Xian-Hua Lin, Lanfen Hu, Hongjie Xu, Yingying Chen, Qingqing Iwamoto, Yutaro Chen, Yen-Wei College of Info. Science and Engineering Ritsumeikan University Shiga Japan School of Information Science and Engineering Shandong Normal University Jinan250358 China Faculty of Science Yamaguchi University Yamaguchi Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Radiology Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou Zhejiang China Zhejiang Lab Research Center for Healthcare Data Science Hangzhou China

ISBN: (纸本)9789811630125

Content based image retrieval (CBIR) that searches for similar images in a large database has been attracting increasing research interest recently, and it has been applied to medical image characterization for sharing experts’ experiences. One challenging task in CBIR is to extract features for effective image representation. To this end, bag-of-visual-words (BoVW) has been proven to be effective to extract middle-level features for image analysis. However, it is necessary to first vectorize the two- or three-dimensional spatial structure for analysis in conventional BoVW and then destroy the spatial relationships of nearby voxels. In this study, we propose a tensor sparse coding method, which is a multilinear generalization of conventional sparse coding (soft assignment in BoVW), to learn features from multi-dimensional medical images. We regard high-dimensional local structures as tensors and propose a K-CP (CANDECOMP/PARAFAC) algorithm to learn an overcomplete tensor dictionary iteratively. By using the learned overcomplete tensor dictionary, sparse coefficients of tensor local structures are calculated by employing the tensor orthogonal matching pursuit (Tensor-OMP) algorithm, which is an extended multilinear version of the conventional vector-based OMP. The proposed method is applied to the retrieval of focal liver lesions (FLLs) by using a medical database consisting of contrast-enhanced multi-phase computer-tomography (CT) images. Experiments show that the proposed tensor sparse coding method achieved better retrieval performance than conventional methods. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tensors

3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning

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arXiv 2024年

作者： Liu, Zhentao Zhao, Huangxuan Qin, Wenhui Zhou, Zhenghong Wang, Xinggang Wang, Wenping Lai, Xiaochun Zheng, Chuansheng Shen, Dinggang Cui, Zhiming School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai Tech Univerisity Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430022 China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science & Engineering Texas A&M University United States

Digital Subtraction Angiography (DSA) is one of the gold standards in vascular disease diagnosing. With the help of contrast agent, time-resolved 2D DSA images deliver comprehensive insights into blood flow information and can be utilized to reconstruct 3D vessel structures. Current commercial DSA systems typically demand hundreds of scanning views to perform reconstruction, resulting in substantial radiation exposure. However, sparse-view DSA reconstruction, aimed at reducing radiation dosage, is still underexplored in the research community. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. In this study, we propose to use a time-agnostic vessel probability field to solve this problem effectively. Our approach, termed as vessel probability guided attenuation learning, represents the DSA imaging as a complementary weighted combination of static and dynamic attenuation fields, with the weights derived from the vessel probability field. Functioning as a dynamic mask, vessel probability provides proper gradients for both static and dynamic fields adaptive to different scene types. This mechanism facilitates a self-supervised decomposition between static backgrounds and dynamic contrast agent flow, and significantly improves the reconstruction quality. Our model is trained by minimizing the disparity between synthesized projections and real captured DSA images. We further employ two training strategies to improve our reconstruction quality: (1) coarse-to-fine progressive training to achieve better geometry and (2) temporal perturbed rendering loss to enforce temporal consistency. Experimental results have demonstrated superior quality on both 3D vessel reconstruction and 2D view synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Blood

Interplay between Physical Layer Security and Blockchain Technology for 5G and Beyond: A Comprehensive Survey

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TechRxiv

TechRxiv 2022年

作者： Ghourab, Esraa M. Jaafar, Wael Bariah, Lina Naser, Shimaa Muhaidat, Sami Yanikomeroglu, Halim The KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates The Department of Software and Information Technology Engineering Ecole de Technologie Supérieure MontrealQCH3C 1K3 Canada Abu Dhabi9639 United Arab Emirates The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada The Department of Systems and Computer Engineering Carleton University OttawaON Canada

Fifth-generation and beyond (5G and xG) wireless networks are envisioned to meet the requirements of vertical applications like high traffic throughput, ultra-massive connectivity, extremely low latency, and high quality of service. Disruptive technologies, such as massive multiple-input multiple-output, millimeter wave, and multiple access are being deployed to meet these requirements. However, their deployment poses several challenges, including a lack of network transparency, management decentralization, and reliability. Moreover, the heterogeneity of future networks raises security concerns, e.g., confidentiality, privacy, and trustworthiness. Indeed, due to the emergence of novel paradigms, e.g., quantum computing, traditional security approaches are no longer sufficient to protect over-the-air communications. Hence, 5G/xG networks must consider smart security techniques to operate seamlessly and efficiently. Within this context, physical layer security (PLS) and blockchain represent promising solutions to complement existing methods. By exploiting the characteristics of wireless links, PLS can enhance the security of communications, while blockchain may enable networks’ decentralization, integrity, and trustworthiness. Motivated by these advancements, we provide an in-depth review of the existing PLS and blockchain literature. Then, for the first time in the literature, we present a framework to integrate PLS with blockchain in 5G/xG systems. We first provide a thorough discussion about the potential of PLS and blockchain for 5G/xG systems. Then, we present our vision of a cross-layer architecture that leverages PLS and blockchain. Through a case study, we demonstrate the high potential of cross-layer design to improve the security of vehicular networks. Finally, we identify related challenges and shed light on future research directions. © 2022, CC BY.

关键词： MIMO systems

Elucidating the role of SLC4A7 in glioma prognosis: A comprehensive approach combining bioinformatics, single-cell analysis, and tissue validation

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Journal of Medical sciences (Taiwan) 2023年第5期43卷 202-211页

作者： Liu, Tung Liu, Nien-Tzu Huang, Yu-Chuan Hsu, Wei-Wen Lin, Yu-Chieh Li, Yao-Feng Department of Pathology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan School of Pharmacy and Institute Pharmacy National Defense Medical Center Taipei Taiwan Department of Research and Development National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Department of Pathology and Laboratory Medicine Taoyuan Armed Forces General Hospital Taoyuan Taiwan Graduate Institute of Medical Sciences National Defense Medical Center Taipei Taiwan Graduate Institute of Life Sciences National Defense Medical Center Taipei Taiwan

Background: Gliomas, prevalent and lethal brain tumors, present limited treatment options despite advancements in understanding their molecular features. SLC4A7, an SLC4 family member and potential biomarker, is involved in acid-base regulation, affecting cancer cell growth. Targeting this mechanism may offer new therapeutic strategies. Aim: This study examines SLC4A7's role in gliomas and its potential as a therapeutic target. Methods: Genomic, whole exon sequencing, and single-cell sequencing datasets from glioma patients were processed and analyzed, followed by gene set enrichment analysis (GSEA). Cellular components and immune cell populations were investigated using 12-cell state and CIBERSORT analyses. Tissue microarray and immunohistochemistry were employed, with an automated semi-quantitative system scoring staining. ANOVA determined the significance of immunostaining scores related to clinical parameters. Results: Our data found increased SLC4A7 in tumor components correlated with higher tumor grading and poorer prognosis. Immunohistochemistry confirmed a relationship between SLC4A7 protein expression with tumor grade and the proliferation index. GSEA linked high SLC4A7 to cell proliferation and inflammation signaling. PIK3CAs were identified as a potential upstream in IDH mutant glioma but not in IDH wildtype. A positive correlation between heightened SLC4A7 expression and tumor mutation burden suggested genomic instability's role in SLC4A7 upregulation. Cellular heterogeneity analysis highlighted the importance of inflammatory cells, particularly macrophage M0. Conclusion: This study emphasizes SLC4A7's significance in adult gliomas, associating increased expression with high tumor grade, poor prognosis, enhanced proliferation, and inflammation. Investigating SLC4A7 may provide insights into cancer biology and contribute to developing innovative therapeutic targets for improved brain tumor treatments. © 2023 Journal of Medical sciences | Published by Wolt

关键词： Chinese glioma genome atlas CIBERSORT gene set enrichment analysis glioblastoma microsatellite instability single-cell sequencing SLC4A7 the cancer genome atlas tumor mutation burden

Deep Self-cleansing for Medical Image Segmentation with Noisy Labels

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arXiv 2024年

作者： Dong, Jiahua Zhang, Yue Wang, Qiuli Tong, Ruofeng Ying, Shihong Gong, Shaolin Zhang, Xuanpu Lin, Lanfen Chen, Yen-Wei College of Computer Science and Technology Zhejiang University Hangzhou China School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou China 7T Magnetic Resonance Translational Medicine Research Center Department of Radiology Southwest Hospital Army Medical University Third Military Medical University Chongqing China Department of Radiology The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan

Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise labels are essential for supervision and significantly impact the performance of models. However, manually delineated labels often contain noise, such as missing labels and inaccurate boundary delineation, which can hinder networks from correctly modeling target characteristics. In this paper, we propose a deep self-cleansing segmentation framework that can preserve clean labels while cleansing noisy ones in the training phase. To achieve this, we devise a gaussian mixture model-based label filtering module that distinguishes noisy labels from clean labels. Additionally, we develop a label cleansing module to generate pseudo low-noise labels for identified noisy samples. The preserved clean labels and pseudo-labels are then used jointly to supervise the network. Validated on a clinical liver tumor dataset and a public cardiac diagnosis dataset, our method can effectively suppress the interference from noisy labels and achieve prominent segmentation performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

Tunning the number of chiral edge channels in a fixed quantum anomalous Hall system

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arXiv 2024年

作者： Deng, Peng Han, Yulei Zhang, Peng Chong, Su Kong Qiao, Zhenhua Wang, Kang L. Beijing Academy of Quantum Information Sciences Beijing100193 China Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Physics Fuzhou University Fujian Fuzhou350108 China International Center for Quantum Design of Functional Materials CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics Department of Physics University of Science and Technology of China Anhui230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Quantum anomalous Hall (QAH) insulators exhibit chiral edge channels characterized by vanishing longitudinal conductance and quantized Hall conductance of Ce2/h, wherein the Chern number C is an integer equal to the number of the parallel chiral edge channels. These chiral edge channels conduct dissipationless transport in QAH insulators, making them pivotal for applications in low-consumption electronics and topological quantum computing. While the QAH effect with multiple chiral edge channels (i.e., C >1) has been demonstrated in multilayers consisting of magnetic topological insulators and normal insulators, the channel number remains fixed for a given sample. Here, we unveil the tunability of the number of chiral edge channels within a single QAH insulator device. By tuning the magnetization of individual layers within the multilayer system, Chern insulating states with different Chern numbers are unveiled. The tunable Chern number was corroborated by our theoretical calculations. Furthermore, we conducted layer-dependent calculations to elucidate the contribution of the Chern number from different layers in the multilayer. Our findings demonstrate an extra degree of freedom in manipulating the chiral edge channels in QAH insulators. This newfound tunability offers extra dimension for the implementation of the QAH-based multi-channel dissipationless transport. © 2024, CC BY.

关键词： Multilayers

Real-life Human Activity Recognition with Tri-axial Accelerometer Data from Smartphone using Hybrid Long Short-Term Memory networks 15

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Real-life Human Activity Recognition with Tri-axial Accelero...

15th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2020

作者： Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand

ISBN: (纸本)9780738130682

Human activity recognition (HAR) has an enthusiastic research field in time-series classification due to its variation of successful applications in various domains. The availability of affordable wearable devices have provided many challenging and interesting research HAR problems. Current researches suggest that deep learning approaches are suited to automated feature extraction from raw sensor data, instead of conventional machine learning approaches that reply on handcrafted features. Based on the recent success of Long Short-Term Memory (LSTM) networks for HAR domains, this work proposes a generic framework for accelerometer data based on LSTM networks for real-life HAR. Four hybrid LSTM networks have been comparatively studied on a public available real-life HAR dataset. Moreover, we take advantage of Bayesian optimization techniques for tuning hyperparameter of each LSTM networks. The experimental results indicate that the CNN-LSTM network surpasses other hybrid LSTM networks. © 2020 IEEE.

关键词： Long short-term memory

Silicon photonics for high-capacity data communications

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Photonics Research 2022年第9期10卷 I0002-I0030页

作者： YAOCHENG SHI YONG ZHANG YATING WAN YU YU YUGUANG ZHANG XIAO HU XI XIAO HONGNAN XU LONG ZHANG BINGCHENG PAN Centre for Optical and Electromagnetic Research State Key Laboratory for Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityZijingang CampusHangzhou 310058China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute for Energy Efficiency University of California Santa BarbaraSanta BarbaraCalifornia 93106USA Electrical and Computer Engineering Department King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Wuhan National Laboratory for Optoelectronics&School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China National Information Optoelectronics Innovation Center China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China State Key Laboratory of Optical Communication Technologies and Networks China Information and Communication Technologies Group Corporation(CICT)Wuhan 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinNew TerritoriesHong KongChina

In recent years,optical modulators,photodetectors,(de)multiplexers,and heterogeneously integrated lasers based on silicon optical platforms have been *** performance of some devices even surpasses the traditional III-V and photonic integrated circuit(PIC)platforms,laying the foundation for large-scale photonic *** photonic technology can overcome the limitations of traditional transceiver technology in high-speed transmission networks to support faster interconnection between data *** this article,we will review recent progress for silicon *** first part gives an overview of recent achievements in silicon *** second part introduces the silicon photonic building blocks,including low-loss waveguides,passive devices,modulators,photodetectors,heterogeneously integrated lasers,and so *** the third part,the recent progress on high-capacity silicon photonic transceivers is *** the fourth part,we give a review of high-capacity silicon photonic networks on chip.

关键词： interconnection passive networks

EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning

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arXiv 2022年

作者： Chen, Li-Chin Chen, Po-Hsun Tsai, Richard Tzong-Han Tsao, Yu Research Center for Information Technology Innovation Academia Sinica 128 Academia Road Section 2 Nankang Taipei115 Taiwan Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City320317 Taiwan

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies based on multiple combinations of EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. The late fusion strategy is deemed to be the most effective approach for simultaneous speech generation and enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech enhancement