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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

IEEE Transactions on Medical Imaging 2025年第6期44卷 2504-2514页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： computerized tomography

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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A dual-mode image sensor using an all-inorganic perovskite nanowire array for standard and neuromorphic imaging

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Journal of Semiconductors 2023年第9期44卷 75-82页

作者： Zhenghao Long Yucheng Ding Xiao Qiu Yu Zhou Shivam Kumar Zhiyong Fan Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina State Key Laboratory of Advanced Displays and Optoelectronics Technologies HKUSTClear Water BayKowloonHong Kong SARChina Department of Chemical and Biological Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong Kong SARChina Shanghai Artificial Intelligence Laboratory Shanghai 200232China

The high-density,vertically aligned retinal neuron array provides effective vision,a feature we aim to replicate with electronic ***,the conventional complementary metal-oxide-semiconductor(CMOS)image sensor,based on separate designs for sensing,memory,and processing units,limits its integration ***,redundant signal communication significantly increases energy *** neuromorphic devices integrating sensing and signal processing show promise in various computer vision applications,but there is still a need for frame-based imaging with good *** this study,we developed a dual-mode image sensor based on a high-density all-inorganic perovskite nanowire *** device can switch between frame-based standard imaging mode and neuromorphic imaging mode by applying different *** unique bias-dependent photo response is based on a well-designed energy band *** biomimetic alignment of nanowires ensures the potential for high-resolution *** further demonstrate the imaging ability,we conducted pattern reconstruction in both modes with a 10×10 crossbar *** study introduces a novel image sensor with high compatibility and efficiency,suitable for various applications including computer vision,surveillance,and robotics.

关键词： mode image inorganic

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SLC2-SLAM: Semantic-guided Loop Closure using Shared Latent Code for NeRF SLAM

arXiv

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arXiv 2025年

作者： Ming, Yuhang Ma, Di Dai, Weichen Yang, Han Fan, Rui Zhang, Guofeng Kong, Wanzeng School of Computer Science Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou Dianzi University Hangzhou310018 China College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China

Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure using Shared Latent Code, dubbed SLC2-SLAM. We argue that latent codes stored in many NeRF SLAM systems are not fully exploited, as they are only used for better reconstruction. In this paper, we propose a simple yet effective way to detect potential loops using the same latent codes as local features. To further improve the loop detection performance, we use the semantic information, which are also decoded from the same latent codes to guide the aggregation of local features. Finally, with the potential loops detected, we close them with a graph optimization followed by bundle adjustment to refine both the estimated poses and the reconstructed scene. To evaluate the performance of our SLC2-SLAM, we conduct extensive experiments on Replica and ScanNet datasets. Our proposed semantic-guided loop closure significantly outperforms the pre-trained NetVLAD and ORB combined with Bag-of-Words, which are used in all the other NeRF SLAM with loop closure. As a result, our SLC2-SLAM also demonstrated better tracking and reconstruction performance, especially in larger scenes with more loops, like ScanNet. Copyright © 2025, The Authors. All rights reserved.

关键词： SLAM robotics

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Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search

arXiv

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arXiv 2025年

作者： Liu, Shengcai Lv, Haoze Wang, Zhiyuan Tang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component called unified neural guidance (UNG). Specifically, UNG guides solution generation across both phases and determines appropriate phase transition timing to effectively combine the complementary strengths of LS and PBS. While trained only on simple distributions with relatively small-scale TSP instances, UNiCS generalizes effectively to challenging TSP benchmarks containing much larger instances (10,000-71,009 nodes) with diverse node distributions entirely unseen during training. Experimental results on the large-scale TSP instances demonstrate that UNiCS consistently outperforms state-of-the-art methods, with its advantage remaining consistent across various runtime budgets. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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An anonymous and fair auction system based on blockchain

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Journal of Supercomputing 2023年第13期79卷 13909-13951页

作者： Ye, Zongli Chen, Chin-Ling Weng, Wei Sun, Hongyu Tsaur, Woei-Jiunn Deng, Yong-Yuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Department of Computer Science Jilin Normal University Siping136000 China State Key Laboratory of Numerical Simulation Siping136000 China Computer Center National Taipei University New Taipei City237303 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City237303 Taiwan

Consumer approval of the online auction paradigm is demonstrated by rapidly expanding online auction transaction volumes. Bidders can access a wider variety of products, and sellers can reach a larger audience through online auction platforms. However, privacy concerns have reduced users’ trust in auction platforms, and complicated disputes have led consumers to lose faith in online auctions, negatively impacting market growth. The emergence of blockchain technology has brought a new approach to solving these problems. Blockchain is a new generation of information technology that is decentralized, transparent, traceable, anti-tampering, and unforgeable, following big data, cloud computing, and artificial intelligence. In this paper, we present a blockchain-based online auction solution as well as an auction protocol that uses ring signatures to guarantee anonymity. Three features summarize this essay: (1) Using blockchain technology to ensure that the auction transaction process is transparent, traceable, tamper-proof, and unforgeable. (2) The ring signature-based anonymous auction protocol allows for concealing bidders’ public keys in a group of public keys. Because malicious individuals are prevented from evaluating data by tracking numerous transactions of a single public key, this significantly protects bidders’ privacy. (3) The proposed scheme effectively resolves disputes that may arise during the auction. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Blockchain

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Directed Acyclic Transformer for Non-Autoregressive Machine Translation 39

Directed Acyclic Transformer for Non-Autoregressive Machine ...

引用

39th International Conference on Machine Learning, ICML 2022

作者： Huang, Fei Zhou, Hao Liu, Yang Li, Hang Huang, Minlie The CoAI Group Tsinghua University China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China ByteDance AI Lab

Non-autoregressive Transformers (NATs) significantly reduce the decoding latency by generating all tokens in parallel. However, such independent predictions prevent NATs from capturing the dependencies between the tokens for generating multiple possible translations. In this paper, we propose Directed Acyclic Transfomer (DA-Transformer), which represents the hidden states in a Directed Acyclic Graph (DAG), where each path of the DAG corresponds to a specific translation. The whole DAG simultaneously captures multiple translations and facilitates fast predictions in a non-autoregressive fashion. Experiments on the raw training data of WMT benchmark show that DA-Transformer substantially outperforms previous NATs by about 3 BLEU on average, which is the first NAT model that achieves competitive results with autoregressive Transformers without relying on knowledge distillation. Copyright Â© 2022 by the author(s)

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Effects of Combined Stress from Salt and Herbicide on Microbial Remediation of Soil Contaminated by n-Hexadecane and Phenanthrene

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China Petroleum Processing & Petrochemical technology 2022年第2期24卷 126-137页

作者： Liu Bingkun Zuo Shuai Wang Ruirui Dong Kangning Zhang Xiuxia Li Ruyue Hao Boyu Li Jing Yin Linghao Bai Yuhao Song Jiayu Gu Yingying Sun Juan State Key Laboratory of Petroleum Pollution Control Beijing 102206 Department of Environment and Safety Engineering China University of PetroleumQingdao 266580 Department of Computer Science and Technology China University of PetroleumQingdao 266580

In this study,physicochemical,cluster,and high-throughput sequencing analyses were used to investigate the joint effects of salt and herbicide(glyphosate)stress factors on the microbial remediation of soil contaminated by n-hexadecane and *** on the soil’s physical and chemical properties,differences in microbial quantity and enzyme activities were analyzed among the samples,and how that influenced distribution of soil community structure was focused *** 120 days of indoor simulated remediation,the cumulative degradation rate of n-hexadecane decreased by 94.92%(blank control),96.96%(low concentration salt and glyphosate stress group),65.07%(high concentration salt and glyphosate stress group),while that of phenanthrene decreased by 87.33%(blank control),86.25%(low concentration salt and glyphosate stress group),58.45%(high concentration salt and glyphosate stress group).The combined stress of salt and herbicides was capable of lowering the reduction efficiency of organic matter,total nitrogen,and total phosphorus,and also restricting the growth of microorganisms and enzyme *** analysis results indicated that the non-stress group was similar to the low-concentration compound stress group during different remedial period,whereas both of those differed starkly from the high-concentration compound stress ***-throughput sequencing revealed that the dominant soil bacteria phyla shifted from Firmicutes to Actinobacteria within 120 days of remediation.

关键词： oil-contaminated soil cluster analysis common quantity sequencing microorganisms salt stress glyphosate stress

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Forecasting-Aided Graphical Learning for Robust state Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and Control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, state Grid Electric Power.

关键词： Electric power distribution

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Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers

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Complex System Modeling and Simulation 2022年第3期2卷 224-237页

作者： Zunjun Wang Zhiyang Jia Xiuxuan Tian Jingchuan Chen Bei Pan State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing Systems Engineering University of Hong KongHong Kong 999077China School of Engineering Tokyo University of TechnologyTokyo 192-0982Japan

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

关键词： flexible assembly system dynamic performance Bernoulli machines finite buffers changeovers

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