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arXiv 2022年

作者： Zhou, Haoran Chen, Honghua Zhang, Yingkui Wei, Mingqiang Xie, Haoran Wang, Jun Lu, Tong Qin, Jing Zhang, Xiao-Ping State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Department of Computing and Decision Sciences Lingnan University Hong Kong School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting-edge learning-based techniques for shape analysis and generation. In this paper, we propose a normal refinement network, called Refine-Net, to predict accurate normals for noisy point clouds. Traditional normal estimation wisdom heavily depends on priors such as surface shapes or noise distributions, while learning-based solutions settle for single types of hand-crafted features. Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations. To this end, several feature modules are developed and incorporated into Refine-Net by a novel connection module. Besides the overall network architecture of Refine-Net, we propose a new multi-scale fitting patch selection scheme for the initial normal estimation, by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal estimation framework: 1) point normals obtained from other methods can be further refined, and 2) any feature module related to the surface geometric structures can be potentially integrated into the framework. Qualitative and quantitative evaluations demonstrate the clear superiority of Refine-Net over the state-of-the-arts on both synthetic and real-scanned datasets. Our code is available at https://***/hrzhou2/refinenet. Copyright © 2022, The Authors. All rights reserved.

关键词： Domain Knowledge

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PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

arXiv

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arXiv 2024年

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Martí, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guénolé Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Víctor M. Lu, Yaosheng Lekadir, Karim Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Heidelberg Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Group University of Girona Girona Spain Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China School of Computer Science & Engineering Chongqing University of Technology Chongqing China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China NanFang Hospital of Southern Medical University Guangzhou China Zhujiang Hospital of Southern Medical University Guangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain Institute The University of Auckland Private Bag 92019 Auckland1142 New Zealand

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging. © 2024, CC0.

关键词： Ultrasonic imaging

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Automatic Grading of the Zurich Pituitary Score: Using Semantic Segmentation and a Seed-Growing Algorithm

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Brain and Spine 2024年 4卷 103523-103523页

作者： Da Mutten, Raffaele Zanier, Olivier Bottini, Massimo Baumann, Yves Ciobanu-Caraus, Olga Regli, Luca Serra, Carlo Staartjes, Victor Machine Intelligence in Clinical Neuroscience (MICN) Laboratory Department of Neurosurgery Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland

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Temporal latent variable structural causal model for causal discovery under external interferences

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Neurocomputing 2025年 640卷

作者： Cai, Ruichu Huang, Xiaokai Chen, Wei Li, Zijian Hao, Zhifeng School of Computer Science Guangdong University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates College of Mathematics and Computer Science Shantou University Shantou China

Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobserved factors that affect the observed data. Specifically, to capture the causal strength and adjacency information, we propose a new temporal latent variable structural causal model, incorporating causal strength and adjacency coefficients that represent the causal relationships between variables. Considering that expert knowledge can provide information about unknown interferences in certain scenarios, we develop a method that facilitates the incorporation of prior knowledge into parameters learning based on Variational Inference, to guide the model estimation. Experimental results demonstrate the stability and accuracy of our proposed method. © 2025 Elsevier B.V.

关键词： Variational techniques

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Automated Cardiac Vortex Ring Identification and Characterization Based on Recurrent All-Pairs Field Transforms and Lagrangian Averaged Vorticity Deviation

SSRN

引用

SSRN 2023年

作者： Yang, Ke Zeng, Shan Shi, Ruizheng Ghista, Dhanjoo N. Hu, Xin Lv, Site Zhang, Chris W.J. Zhang, Yi Wong, Kelvin K.L. School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Cardiovascular Medicine Xiangya Hospital Central South University Changsha410008 China University 2020 Foundation San JoseCA95126 United States The State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering College of Engineering University of Saskatchewan SaskatoonSKS7N 5A9 Canada Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610041 China School of Electrical and Electronic Engineering University of Adelaide AdelaideSA5005 Australia

Automated identification of cardiac vortices is a formidable task due to the complex nature of blood flow within the heart chambers. This study proposes a novel approach that algorithmically characterizes the identification criteria of these cardiac vortices based on Lagrangian Averaged Vorticity Deviation (LAVD). For this purpose, the Recurrent All-Pairs Field Transforms (RAFT) on the optical flow of Phase Contrast Magnetic Resonance Imaging (PC-MRI) is introduced to construct a continuous velocity field and reduce errors that arise from the integral process of LAVD. Additionally, Generalized Hough Transform (GHT) is applied for automated depiction of the structure of cardiac vortices. The effectiveness of this method is demonstrated and validated by the computation of the acquired cardiac flow data. The results of this comprehensive visual and analytical study show that the evolution of cardiac vortices can be effectively described and displayed, and the RAFT framework for optical flow can synthesize the in-between PC-MRIs with high accuracy. This allows cardiologists to acquire a deeper understanding of intracardiac hemodynamics and its impact on cardiac functional performance. © 2023, The Authors. All rights reserved.

关键词： Vortex flow

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Visual object tracking with discriminative filters and siamese networks: A survey and outlook

arXiv

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arXiv 2021年

作者： Javed, Sajid Danelljan, Martin Khan, Fahad Shahbaz Khan, Muhammad Haris Felsberg, Michael Matas, Jiri The EECS Department Khalifa University of Science and Technology P.O Box: 127788 Abu Dhabi United Arab Emirates The Computer Vision Lab Dept. of Information Technology and Electrical Engineering ETH Zürich Switzerland Computer Vision Department MBZUAI Abu Dhabi United Arab Emirates Computer Vision Laboratory Linköping University Sweden Center for Machine Perception Czech Technical University Prague Czech Republic

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis. © 2021, CC BY-SA.

关键词： Surveys

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Joint Parking and Power Management for Electric Vehicle Edge Computing: A Bilevel Optimization Approach

Joint Parking and Power Management for Electric Vehicle Edge...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xumin Huang Weifeng Zhong Jiangtian Nie Jiawen Kang Zehui Xiong Yuan Wu Mohsen Guizani School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer and Information Science University of Macau Macau China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

关键词： Wireless communication Vehicle-to-grid Systematics Power system management Computational modeling Transforms Relaxation methods

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Deep Learning Radiomics Based on DCE-MRI Predicts Response of Conversion Therapy for Unresectable Hepatocellular Carcinoma

SSRN

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SSRN 2024年

作者： Liu, Han Zhang, Jicong Zou, Liwen Yin, Jing Wan, Peng Zhang, Yu Li, Zhengran Kong, Wentao Department of Ultrasound Nanjing Drum Tower Hospital Affiliated Hospital of Medical School Nanjing University Nanjing210002 China Department of Interventional Radiology the Third Affiliated Hospital of Sun Yat-sen University Guangzhou510630 China Department of Mathematics Nanjing University Nanjing210008 China Department of Pain Medicine Nanjing Jinling Hospital Affiliated Hospital of Medical School Nanjing University Nanjing210002 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China

Background: To develop and validate a deep learning radiomics nomogram (DLRN) for selecting unresectable patients with hepatocellular carcinoma (HCC) who can be transformed to resection within 4 cycles of conventional transarterial chemoembolization (c-TACE) based conversion therapy. Methods: Pretreatment contrast-enhanced MRI were retrospectively acquired in unresectable HCC patients who received c-TACE based conversion therapy from August 2017 to April 2023. The training cohort (TC) and internal validation cohort (IVC), comprising 190 and 81 patients, respectively, were randomly selected from Hospital 1. The external validation cohort (EVC) was comprised of 82 patients from Hospital 2 and 3. The deep learning radiomics signature (DLRS) and clinical model were constructed using a three-dimensional convolutional neural network and independent clinical factors, respectively. Multivariable logistic regression analysis was performed to develop the DLRN by integrating the DLRS with the clinical model. Prognostic information, including overall survival (OS) and progression-free survival (PFS), was analyzed by Kaplan-Meier curves and Cox regression. The performance of the models was assessed with respect to their discrimination and clinical utility. Findings: The DLRN yielded a satisfactory performance with areas under the receiver operating curve of 0.992 (95% CI, 0.982–1.000), 0.893 (95% CI, 0.822–0.965), and 0.929 (95% CI, 0.878–0.981) in the TC, IVC, and EVC, respectively. Furthermore, the DLRN exhibited superior performance compared to the clinical model in all cohorts (Delong P © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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TIDE: Test Time Few Shot Object Detection

arXiv

引用

arXiv 2023年

作者： Li, Weikai Wei, Hongfeng Wu, Yanlai Yang, Jie Ruan, Yudi Li, Yuan Tang, Ying The School of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing210016 China China Science IntelliCloud Technology Co. Ltd. Anhui230000 China The China Science IntelliCloud Technology Co. Ltd. Anhui230000 China The School of Information Science and Technology Chongqing Jiaotong University Chongqing400074 China The Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States

Few-shot object detection (FSOD) aims to extract semantic knowledge from limited object instances of novel categories within a target domain. Recent advances in FSOD focus on fine-tuning the base model based on a few objects via meta-learning or data augmentation. Despite their success, the majority of them are grounded with parametric readjustment to generalize on novel objects, which face considerable challenges in Industry 5.0, such as (i) a certain amount of fine-tuning time is required, and (ii) the parameters of the constructed model being unavailable due to the privilege protection, making the fine-tuning fail. Such constraints naturally limit its application in scenarios with real-time configuration requirements or within black-box settings. To tackle the challenges mentioned above, we formalize a novel FSOD task, referred to as Test TIme Few Shot DEtection (TIDE), where the model is un-tuned in the configuration procedure. To that end, we introduce an asymmetric architecture for learning a support-instance-guided dynamic category classifier. Further, a cross-attention module and a multi-scale resizer are provided to enhance the model performance. Experimental results on multiple few-shot object detection platforms reveal that the proposed TIDE significantly outperforms existing contemporary methods. The implementation codes are available at https://***/deku-0621/TIDE. © 2023, CC BY-NC-ND.

关键词： Object detection

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