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JACC: Advances 2023年第2期2卷 100267-100267页

作者： Tandon, Animesh Nguyen, Hoang H. Avula, Sravani Seshadri, Dhruv R. Patel, Akash Fares, Munes Baloglu, Orkun Amdani, Shahnawaz Jafari, Roozbeh Inan, Omer T. Drummond, Colin K. Department of Pediatric Cardiology Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Cleveland Clinic Children's Center for Artificial Intelligence (C4AI) Cleveland Clinic Children's Cleveland OH United States Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic Cleveland OH United States Department of Biomedical Engineering Case School of Engineering at Case Western Reserve University Cleveland OH United States Division of Cardiology Department of Pediatrics UT Southwestern Medical Center Dallas TX United States Department of Orthopaedics University Hospitals Cleveland Medical Center Cleveland OH United States Department of Critical Care Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Departments of Biomedical Engineering Computer Science and Electrical Engineering Texas A&M University College Station TX United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD;how clinical and research uses of wearable biosensors must consider various CHD physiologies;the technical challenges in developing wearable biosensors for CHD;and special considerations for digital biomarkers in CHD. © 2023 The Authors

关键词： artificial intelligence congenital heart disease wearable biosensors wearables

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Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

arXiv

引用

arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

arXiv

引用

arXiv 2022年

作者： Zhang, Jiayi Zhang, Jing Björnson, Emil ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering Linköping University Linköping Sweden The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free massive multiple-input multiple-output (MIMO) provides more uniform spectral efficiency (SE) for users (UEs) than cellular technology. The main challenge to achieve the benefits of cell-free massive MIMO is to realize signal processing in a scalable way. In this paper, we consider scalable full-pilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF (PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF, and PWPFZF combining with large-scale fading decoding over independent Rayleigh fading channels, taking channel estimation errors and pilot contamination into account. Moreover, we investigate the impact of the number of pilot sequences, antennas per AP, and APs on the performance. Numerical results show that LRZF provides the highest SE. However, PWPFZF is preferable when the number of pilot sequences is large and the number of antennas per AP is small. The reason is that PWPFZF has lower computational complexity and the SE expression can be computed in closed-form. Furthermore, we investigate the performance of PWPFZF combining with fractional power control and the numerical results show that it improves the performance of weak UEs and realizes uniformly good service for all UEs in a scalable fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Power control

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A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions

arXiv

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arXiv 2025年

作者： Zhao, Shuxian Gui, Jie Dong, Minjing Yu, Baosheng Gui, Zhipeng Dong, Lu Tang, Yuan Yan Kwok, James Tin-Yau School of Cyber Science d Engineering Southeast University Nanjing210000 China School of Cyber Science and Engineering Southeast University Nanjing211189 China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing210000 China Department of Computer Science City University of Hong Kong Hong Kong Lee Kong Chian School of Medicine Nanyang Technological University Singapore School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Collaborative Innovation Center of Geospatial Technology Wuhan University Wuhan430079 China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

—The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distribution, which leads to poor model performance. In addition, indistinct inter-class feature distributions further complicate classification tasks. Existing methods often rely on algorithmic heuristics without sufficiently analyzing the underlying data characteristics. We argue that a detailed analysis from the data perspective is essential before developing an appropriate solution. Therefore, this paper proposes a systematic analytical framework for the S&I problem. We first summarize imbalance metrics and complexity analysis methods, highlighting the need for interpretable benchmarks to characterize S&I problems. Second, we review recent solutions for conventional, complexity-based, and extreme S&I problems, revealing methodological differences in handling various data distributions. Our summary finds that resampling remains a widely adopted solution. However, we conduct experiments on binary and multiclass datasets, revealing that classifier performance differences significantly exceed the improvements achieved through resampling. Finally, this paper highlights open questions and discusses future trends. Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Towards Simple and Accurate Human Pose Estimation with Stair Network

arXiv

引用

arXiv 2022年

作者： Jiang, Chenru Huang, Kaizhu Zhang, Shufei Wang, Xinheng Xiao, Jimin Niu, Zhenxing Hussain, Amir Department of Computer Science University of Liverpool LiverpoolL69 7ZX United Kingdom Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou215123 China Data Science Research Center Duke Kunshan University No. 8 Duke Avenue Kunshan215316 China School of Computing Edinburgh Napier University EdinburghEH11 4BN United Kingdom School of Electronic Engineering Xidian University Xian710000 China

In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcome this limitation, we develop a small yet discrimicative model called STair Network, which can be simply stacked towards an accurate multistage pose estimation system. Specifically, to reduce computational cost, STair Network is composed of novel basic feature extraction blocks which focus on promoting feature diversity and obtaining rich local representations with fewer parameters, enabling a satisfactory balance on efficiency and performance. To further improve the performance, we introduce two mechanisms with negligible computational cost, focusing on feature fusion and replenish. We demonstrate the effectiveness of the STair Network on two standard datasets, e.g., 1-stage STair Network achieves a higher accuracy than HRNet by 5.5% on COCO test dataset with 80% fewer parameters and 68% fewer GFLOPs. © 2022, CC BY.

关键词： Cost effectiveness

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Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

arXiv

引用

arXiv 2025年

作者： Hou, Qingshan Zhou, Yukun Goh, Jocelyn Hui Lin Zou, Ke Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Lo, Thaddaeus Lei, Xiaofeng Wagner, Siegfried K. Chia, Mark A. Yang, Dawei Jiang, Hongyang Ran, AnRan Santos, Rui Somfai, Gabor Mark Zhou, Juan Helen Chen, Haoyu Chen, Qingyu Cheung, Carol Yim-Lui Keane, Pearse A. Tham, Yih Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang China Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Singapore Institute of Ophthalmology University College London London United Kingdom Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Hong Kong Department of Ophthalmology Stadtspital Zürich Zurich Switzerland Spross Research Institute Zurich Switzerland Department of Ophthalmology Semmelweis University Budapest Hungary Centre for Sleep and Cognition Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Joint Shantou International Eye Center Shantou University the Chinese University of Hong Kong Guangdong Shantou China Shantou University Medical College Guangdong Shantou China Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States Duke-NUS Medical School Singapore

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by finetuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850–0.952 vs. 0.823-0.944, across three datasets, all P≤0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCai 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

arXiv

引用

arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCai CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Statistical modeling: The three cultures

arXiv

引用

arXiv 2020年

作者： Daoud, Adel Dubhashi, Devdatt Department of Sociology and Work Science University of Gothenburg Gothenburg Sweden Center for Population and Development Studies Harvard University Cambridge United States Data Science and AI division Department of Computer Science and Engineering Gothenburg Sweden

Two decades ago, Leo Breiman (2001) identified two cultures for statistical modeling. The data modeling culture (DMC) refers roughly to practices aiming to conduct statistical inference, βĜ, on one or several quantities of interest. The algorithmic modeling culture (AMC) refers to practices defining a machine-learning (ML) procedure that generates accurate predictions, ŶŶ, about an event of interest (outcome). Breiman argued that statisticians should give more attention to AMC than to DMC, because AMC provides novel procedures for prediction. While twenty years later, DMC has perhaps lost some of its dominant role in statistics because of the data-science revolution, we observe that this culture is still the modus operandi—the leading practice of a group—in the natural and social sciences. DMC is the modus operandi because of the influence of the established scientific method, called the hypothetico-deductive scientific method. Despite the incompatibilities of AMC with the hypothetico-deductive scientific method, among some research groups, AMC and DMC cultures mix intensely. We argue that this mixing has formed a fertile spawning pool for a mutated culture: a hybrid modeling culture (HMC) where prediction and inference have fused into new procedures where they reinforce one another. This article identifies key characteristics of HMC, thereby facilitating the scientific endeavor and fueling the evolution of statistical cultures towards better practices. By better, we mean increasingly reliable, valid, and efficient statistical practices in analyzing causal relationships. In combining inference and prediction, the result of HMC is that the distinction between ŶŶ and βĜ—taken to its limit—melts away. We qualify our "melting-away" argument by describing three HMC practices, where each practice captures an aspect of the scientific cycle: ML for causal inference, ML for data acquisition, and ML for theory prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey

arXiv

引用

arXiv 2025年

作者： Cai, Lingyi Wang, Jiacheng Zhang, Ruichen Zhang, Yu Jiang, Tao Niyato, Dusit Wang, Xianbin Jamalipour, Abbas Shen, Xuemin Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, ai-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet. Copyright © 2025, The Authors. All rights reserved.

关键词： aircraft communication

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