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Robotics and Autonomous Systems 2024年 178卷

作者： Arodh Lal Karn Sudhakar Sengan Ketan Kotecha Irina V Pustokhina Denis A Pustokhin V Subramaniyaswamy Dharam Buddhi Department of Financial and Actuarial Mathematics School of Science Xi'an Jiaotong-Liverpool University China Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune Maharashtra 412115 India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics 117997 Moscow Russia Department of Logistics State University of Management 109542 Moscow Russia School of Computing SASTRA Deemed University Thanjavur 613401 Tamil Nadu India Division of Research & Innovation Uttaranchal University Uttarakhand 248007 Dehradun India

A multi-objective deep reinforcement learning algorithm for spatio-temporal latency optimization in mobile IoT-enabled edge computing networks

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Parisa Khoshvaght Amir Haider Amir Masoud Rahmani Farhad Soleimanian Gharehchopogh Ferzat Anka Jan Lansky Mehdi Hosseinzadeh Institute of Research and Development Duy Tan University Da Nang Vietnam School of Engineering & Technology Duy Tan University Da Nang Vietnam Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of AI and Robotics Sejong University Seoul 05006 Republic of Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Ur. C. Islamic Azad University Urmia Iran Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

关键词： Double deep Q-learning Latency and energy efficiency Mobile edge computing Spatio-temporal optimization

Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI

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Medical image analysis 2025年 104卷 103630页

作者： Nicolás Gaggion Benjamin A Matheson Yan Xia Rodrigo Bonazzola Nishant Ravikumar Zeike A Taylor Diego H Milone Alejandro F Frangi Enzo Ferrante Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK. Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina. Christabel Pankhurst Institute The University of Manchester Manchester UK Centre for Computational Imaging and Modelling in Medicine (CIMIM) Department of Computer Science School of Engineering and Division of Informatics Imaging and Data Science School of Health Sciences The University of Manchester Manchester UK NIHR Manchester Biomedical Research Centre Manchester Academic Health Sciences Centre University of Manchester Manchester UK Medical Imaging Research Centre (MIRC) Department of Cardiovascular Sciences KU Leuven Leuven Belgium. Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Electronic address: eferrante@sinc.unl.edu.ar.

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images and then reconstructing meshes, making them time-consuming and prone to error propagation. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multi-view HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity meshes from CMR images. Multi-view HybridVNet outperforms the state-of-the-art, achieving improvements of up to ∼27% reduction in Mean Contour Distance (from 1.86 mm to 1.35 mm for the LV Myocardium), up to ∼18% improvement in Hausdorff distance (from 4.74 mm to 3.89 mm, for the LV Endocardium), and up to ∼8% in Dice Coefficient (from 0.78 to 0.84, for the LV Myocardium), highlighting its superior accuracy.

关键词： Cardiac imaging Geometric deep learning Hybrid graph convolutional neural network Volume-to-mesh

3D Reconstruction of human brain post-mortem photography and integration of dementia histopathology

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Alzheimer's & Dementia 2023年第S12期19卷

作者： Diana Ortega-Cruz Kimberly S. Bress Harshvardhan Gazula Alberto Rabano Juan Eugenio Iglesias Bryan A Strange CIEN Foundation Queen Sofia Foundation Alzheimer Centre Madrid Spain Centre for Biomedical Technology Universidad Politécnica de Madrid Madrid Spain Vanderbilt University School of Medicine Nashville TN USA Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Charlestown MA USA Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Centre for Biomedical Technology Universidad Politecnica de Madrid Madrid Spain

Background In Alzheimer’s Disease (AD) and other types of dementia, neuropathological assessment serves as ground truth for diagnosis and validation of novel biomarkers. Such assessment commonly implies inspection of a single histological slide per region of interest. Pathology burden quantification, as well as investigation of its spatial distribution, is thus biased by the variability of tissue sampling in routinary neuropathological practice. Numerous efforts have sought to overcome this lack of generalizability using 3D reconstruction of histology, but available approaches rely on dense sectioning and an MRI scan, which may not be attainable by many brain banks. Method We present a histology mapping pipeline that uses blockface images of brain hemisphere slices for 3D spatial localization. The slices are first geometrically stacked and coarsely aligned using a probabilistic atlas. Next, deep learning contrast synthesis is employed to obtain a high-resolution 3D image of the hemisphere with MRI-like contrast, which is subsequently registered nonlinearly to a standard atlas (MNI). Histological slides are then registered to the corresponding coronal slice within the subject-specific reconstructed hemisphere. Finally, the registrations are concatenated to deform the histology to the standard space. We show the applicability of this pipeline by analyzing the distribution of tau AD pathology along the longitudinal axis of the hippocampus in 26 patients with no significant co-pathologies aside from AD. Result We took advantage of the inter-subject spatial variability observed after 3D slide mapping to achieve extensive sampling along the hippocampal long axis. Our results show an anterior-posterior gradient of tau burden in the hippocampus that is consistent with previous work using ex vivo MRI and dense histological sampling, further supporting the reliability of the pipeline. Conclusion Our pipeline enables studying pathology in 3D using sparse sampling and in absenc

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Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)

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arXiv 2023年

作者： Waldmannstetter, Diana Ezhov, Ivan Wiestler, Benedikt Campi, Francesco Kukuljan, Ivan Ehrlich, Stefan Vinayahalingam, Shankeeth Baheti, Bhakti Chakrabarty, Satrajit Baid, Ujjwal Bakas, Spyridon Schwarting, Julian Metz, Marie Kirschke, Jan S. Rueckert, Daniel Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany Helmholtz AI Helmholtz Munich Neuherberg Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Healthcare Technologies SETLabs Research Munich Germany Department of Oral and Maxillofacial Surgery Radboud University Nijmegen Medical Centre Nijmegen Netherlands Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St.Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden

Accurate image registration is pivotal in bio-medical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed Landmark Hit Rate (HitR), which focuses on the clinical relevance of image registration accuracy. Unlike traditional metrics such as Target Registration Error, which emphasize sub-resolution differences, HitR considers whether registration algorithms successfully position landmarks within defined confidence zones. This paradigm shift acknowledges the inherent annotation noise in medical images, allowing for more meaningful assessments. To equip HitR with label-noise-awareness, we propose defining these confidence zones based on an Inter-rater Variance analysis. Consequently, hit rate curves are computed for varying landmark zone sizes, enabling performance measurement for a task-specific level of accuracy. Our approach offers a more realistic and meaningful assessment of image registration algorithms, reflecting their suitability for clinical and biomedical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

Randomised benchmarking for characterizing and forecasting correlated processes

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arXiv 2023年

作者： Zhang, Xinfang Wu, Zhihao White, Gregory A.L. Xiang, Zhongcheng Hu, Shun Peng, Zhihui Liu, Yong Zheng, Dongning Fu, Xiang Huang, Anqi Poletti, Dario Modi, Kavan Wu, Junjie Deng, Mingtang Guo, Chu Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Physics and Astronomy Monash University VIC3800 Australia Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute of Physics Chinese Academy of Sciences Beijing100190 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China Science Mathematics and Technology Cluster and Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Centre for Quantum Technologies National University of Singapore 117543 Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Quantum for NSW Sydney2000 Australia

The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Quantum circuit architecture search for variational quantum algorithms

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arXiv 2020年

作者： Du, Yuxuan Huang, Tao You, Shan Hsieh, Min-Hsiu Tao, Dacheng Ubtech Sydney Ai Centre School of Computer Science Faculty of Engineering The University of Sydney NSW2008 Australia Jd Explore Academy Beijing101111 China SenseTime Beijing100080 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney NSW2007 Australia Hon Hai Quantum Computing Research Center Taipei114 Taiwan

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze. © 2020, CC BY-NC-ND.

关键词： Timing circuits

Accurate super-resolution low-field brain MRI

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arXiv 2022年

作者： Iglesias, Juan Eugenio Schleicher, Riana Laguna, Sonia Billot, Benjamin Schaefer, Pamela McKaig, Brenna Goldstein, Joshua N. Sheth, Kevin N. Rosen, Matthew S. Kimberly, W. Taylor Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Neurology and Center for Genomic Medicine Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Zurich Switzerland Department of Emergency Medicine Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Department of Neurology Yale New Haven Hospital New HavenCT06511 United States Department of Physics Harvard University CambridgeMA02138 United States

The recent introduction of portable, low-field MRI (LF-MRI) into the clinical setting has the potential to transform neuroimaging. However, LF-MRI is limited by lower resolution and signal-to-noise ratio, leading to incomplete characterization of brain regions. To address this challenge, recent advances in machine learning facilitate the synthesis of higher resolution images derived from one or multiple lower resolution scans. Here, we report the extension of a machine learning super-resolution (SR) algorithm to synthesize 1 mm isotropic MPRAGE-like scans from LF-MRI T1-weighted and T2-weighted sequences. Our initial results on a paired dataset of LF and high-field (HF, 1.5T-3T) clinical scans show that: (i) application of available automated segmentation tools directly to LF-MRI images falters;but (ii) segmentation tools succeed when applied to SR images with high correlation to gold standard measurements from HF-MRI (e.g., r = 0.85 for hippocampal volume, r = 0.84 for the thalamus, r = 0.92 for the whole cerebrum). This work demonstrates proof-of-principle post-processing image enhancement from lower resolution LF-MRI sequences. These results lay the foundation for future work to enhance the detection of normal and abnormal image findings at LF and ultimately improve the diagnostic performance of LF-MRI. Our tools are publicly available on FreeSurfer (***/). © 2022, CC BY-NC-ND.

关键词： Brain

Quantum Differentially Private Sparse Regression Learning

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arXiv 2020年

作者： Du, Yuxuan Hsieh, Min-Hsiu Liu, Tongliang You, Shan Tao, Dacheng School of Computer Science Faculty of Engineering The University of Sydney JD Explore Academy Beijing101111 China Hon Hai Quantum Computing Research Center Taipei114 Taiwan Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Sydney2006 Australia SenseTime Beijing100080 China

The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy. To fill this knowledge gap, here we devise an efficient quantum differentially private (QDP) Lasso estimator to solve sparse regression tasks. Concretely, given N d-dimensional data points with N d, we first prove that the optimal classical and quantum non-private Lasso requires Ω(N+d) and Ω(√N+ √d) runtime, respectively. We next prove that the runtime cost of QDP Lasso is dimension independent, i.e., O(N5/2), which implies that the QDP Lasso can be faster than both the optimal classical and quantum non-private Lasso. Last, we exhibit that the QDP Lasso attains a near-optimal utility bound Õ(N−2/3) with privacy guarantees and discuss the chance to realize it on near-term quantum chips with advantages. © 2020, CC BY-NC-ND.

关键词： Machine learning