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ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images

computer Methods and Programs in Biomedicine Update 2024年 5卷

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Nnani, Ann O Deng, Fuhu Ejiyi, Thomas Ugochukwu Ejiyi, Makuachukwu Bennedith Agbesi, Victor Kwaku Bamisile, Olusola Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China College of Environmental Science and Engineering Hohai University Nanjing China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu Nigeria Pharmacy Department University of Nigeria Nsukka Enugu Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre Chengdu University of Technology Chengdu China

Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals. © 2023

关键词： Chest X-ray images External Attention ResfEANet ResNet Tuberculosis

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Driving force of atomic ordering in Fe1−xPtx, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

arXiv

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arXiv 2024年

作者： Tsuyama, Tomoyuki Kaneshita, Takeshi Matsui, Akira Ochiai, Kohei Tanaka, Hiroaki Kondo, Ryohei Fukushima, Takayuki Ohashi, Haruhisa Hashimoto, Atsushi Okuno, Yoshishige Zhu, Jian-Gang Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & development Center 5-1 Yawatakaigan-dori Ichihara Chiba290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We report the mechanisms of atomic ordering in Fe1−xPtx alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phases, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at a finite temperature. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions, whereas the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order. © 2024, CC BY-SA.

关键词： Spin polarization

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The Privacy-Value-App Relationship and the Value-centered Privacy Assistant

arXiv

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arXiv 2023年

作者： Carter, Sarah E. Tiddi, Ilaria d’Aquin, Mathieu Cormican, Kathryn Spagnuelo, Dayana Felzmann, Heike D-REAL Discipline of Philosophy Data Science Institute University of Galway Ireland Computer Science Department Vrije Universiteit Amsterdam Netherlands LORIA Université de Lorraine France Enterprise Research Center School of Engineering University of Galway Ireland TNO Netherlands Discipline of Philosophy University of Galway Ireland

Many of us make quick decisions that affect our data privacy on our smartphones without due consideration of our values. One such decision point is establishing whether to download a smartphone app or not. In this work, we aim to better understand the relationship between our values, our privacy preferences, and our app choices, as well as explore the effectiveness of a smartphone value-centered privacy assistant (VcPA) at promoting value-centered app selection. To do this, we conducted a mixed-methods study that involved two phases: The first was an online survey of 273 smartphone user’s values and privacy preferences when considering whether to download one of two apps (Lose It! and OpenLitterMap). Our results suggest that values and privacy preferences are related in an app or context-dependent manner. We found weak but potentially interesting correlations between privacy preferences, values, and which app is being considered, with the strongest correlations occurring between which app was being considered and a user’s values (for example: OpenLitterMap and the value, Universalism). When looking at the value-privacy preference relationship within each app, additional correlations were observed and different values were considered more relevant depending on the app being considered. The second phase was testing the VcPA with 77 users in a synthetic Mock App Store setting. We established usability of a VcPA, with the VcPA helping some users more than others with selecting apps consistent with their selected value profile. Improvements based on participants feedback included helping users better compare apps to select one that fits both their desired function and value set as well as improving upon the presentation of privacy preferences. While the significance of this study lies in progressing our understanding of a value-centered approach to privacy and establishing the usability of the VcPA for a subset of users, results also indicated the need for a more context

关键词： Smartphones

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The DKU-DukeECE-Lenovo system for the diarization task of the 2021 VoxCeleb speaker recognition challenge

arXiv

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arXiv 2021年

作者： Wang, Weiqing Cai, Danwei Lin, Qingjian Yang, Lin Wang, Junjie Wang, Jin Li, Ming Data Science Research Center Duke Kunshan University Kunshan China AI Lab. Lenovo Research Beijing China Department of Electrical and Computer Engineering Duke University Durham United States

This report describes the submission of the DKU-DukeECE-Lenovo team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2021 track 4. Our system includes a voice activity detection (VAD) model, a speaker embedding model, two clustering-based speaker diarization systems with different similarity measurements, two different overlapped speech detection (OSD) models, and a target-speaker voice activity detection (TS-VAD) model. Our final submission, consisting of 5 independent systems, achieves a DER of 5.07% on the challenge test set. © 2021, CC0.

关键词： Speech recognition

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Methodology,progress and challenges of geoscience knowledge graph in International Big science Program of Deep-Time Digital Earth

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Journal of Geographical sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China University of Chinese Academy of Sciences Beijing 100049China School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing 210023China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Command Center of Natural Resources Comprehensive Survey China Geological SurveyBeijing 100055China School of Computer Science China University of GeosciencesWuhan 430074China

Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary *** advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological sciences initiated the“Deeptime Digital Earth International Big science Program”(DDE)in *** the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big *** DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience *** its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FaiR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.

关键词： deep-time Earth geoscience knowledge graph Deep-time Digital Earth International Big science Program

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Transformers in medical image analysis

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Intelligent Medicine 2023年第1期3卷 59-78页

作者： Kelei He Chen Gan Zhuoyuan Li Islem Rekik Zihao Yin Wen Ji Yang Gao Qian Wang Junfeng Zhang Dinggang Shen Medical School of Nanjing University NanjingJiangsu 210093China National Institute of Healthcare Data Science at Nanjing University NanjingJiangsu 210093China BASIRA Laboratory Faculty of Computer and Informatics EngineeringIstanbul Technical UniversityIstanbulTurkey School of Science and Engineering ComputingUniversity of DundeeUK State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingJiangsu 210093China School of Biomedical Engineering ShanghaiTech UniversityShanghai 201210China Department of Research and Development Shanghai United Imaging Intelligence Co.Ltd.Shanghai 200030China Shanghai Clinical Research and Trial Center Shanghai 201703China

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer *** the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and *** paper aimed to promote awareness of the applications of transformers in medical image ***,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic ***,we reviewed various transformer architectures tailored for medical image applications and discuss their *** this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other *** hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

关键词： Transformer Medical image analysis Deep learning Diagnosis Registration Segmentation Image synthesis Multi-task learning Multi-modal learning Weakly-supervised learning

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Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

arXiv

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arXiv 2022年

作者： Li, Hongkang Wang, Meng Liu, Sijia Chen, Pin-Yu Xiong, Jinjun Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute NY United States Department of Computer Science and Engineering Michigan State University MI United States MIT-IBM Watson AI Lab IBM Research MA United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States Department of Computer Science and Engineering University at Buffalo NY United States

Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data. To address its scalability issue due to the recursive embedding of neighboring features, graph topology sampling has been proposed to reduce the memory and computational cost of training GCNs, and it has achieved comparable test performance to those without topology sampling in many empirical studies. To the best of our knowledge, this paper provides the first theoretical justification of graph topology sampling in training (up to) three-layer GCNs for semi-supervised node classification. We formally characterize some sufficient conditions on graph topology sampling such that GCN training leads to a diminishing generalization error. Moreover, our method tackles the non-convex interaction of weights across layers, which is under-explored in the existing theoretical analyses of GCNs. This paper characterizes the impact of graph structures and topology sampling on the generalization performance and sample complexity explicitly, and the theoretical findings are also justified through numerical experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Optimal Qubit Mapping Search for Encoding Classical data into Matrix Product State Representation with Minimal Loss

arXiv

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arXiv 2024年

作者： Jeon, Hyungjun Lee, Kyungmin Lee, Dongkyu Kim, Bongsang Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Quantum AI Dept AI Lab CTO LG Electronics Seoul06772 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing. © 2024, CC BY-NC-ND.

关键词： Mapping

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application of the path optimization method to a discrete spin system

arXiv

引用

arXiv 2023年

作者： Kashiwa, Kouji Namekawa, Yusuke Ohnishi, Akira Takase, Hayato Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan

The path optimization method, which is proposed to control the sign problem in quantum field theories with continuous degrees of freedom by machine learning, is applied to a spin model with discrete degrees of freedom. The path optimization method is applied by replacing the spins with dynamical variables via the Hubbard-Stratonovich transformation, and the sum with the integral. The one-dimensional (Lenz-)Ising model with a complex coupling constant is used as a laboratory for the sign problem in the spin model. The average phase factor is enhanced by the path optimization method, indicating that the method can weaken the sign problem. Our result reproduces the analytic values with controlled statistical errors. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model

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Comparative validation of ai and non-ai methods in MRI volumetry to diagnose Parkinsonian syndromes (vol 13, 3439, 2023)

引用

SCIENTIFIC REPORTS 2023年第1期13卷 1-13页

作者： Song, Joomee Hahm, Juyoung Lee, Jisoo Lim, Chae Yeon Chung, Myung Jin Youn, Jinyoung Cho, Jin Whan Ahn, Jong Hyeon Kim, Kyungsu Department of Neurology and Neuroscience Center Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Republic of Korea Medical AI Research Center Research Institute for Future Medicine Samsung Medical Center Seoul Republic of Korea Department of Biostatistics Columbia University New York NY USA Department of Electrical and Computer Engineering University of Maryland College Park MD USA Department of Medical Device Management and Research SAIHST Sungkyunkwan University Seoul Republic of Korea Department of Radiology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Republic of Korea Department of Data Convergence and Future Medicine Sungkyunkwan University School of Medicine Seoul Republic of Korea Department of Radiology Massachusetts General Brigham and Harvard Medical School Boston MA USA

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson’s disease (PD) and Parkinson’s plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (\(n=105\)) and patients with PD (\(n=105\)), multiple systemic atrophy (\(n=132\)), and progressive supranuclear palsy (\(n=69\)) at Samsung Medical center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

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