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Improving Nonlocal Means Method via a No-Reference Image Content Metric for MRI Denoising

Chinese Journal of Electronics 2025年第4期23卷 735-741页

作者： Xin Hou Jianwu Li Yao Lu Zhengchao Dong Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China Brain Imaging Lab Columbia University New York NY United States New York State Psychiatric Institute New York NY United States

The patch matching of the traditional Non-local means (NLM) filter mainly depends on structure similarity and cannot adapt to the patch rotation or mirroring transformation. Therefore, designing a measure with rotationally invariant similarity is of significant importance for improving the effectiveness of patch comparison of NLM. This paper proposes to apply a no-reference image content metric with the rotation-invariance to NLM for denoising Magnetic resonance (MR) images. The metric measures quantitatively the content of a patch in an image, including sharpness, contrast, and geometric features such as textures and edges. The metric values for every patch are computed and added into the Gaussian matching kernel of NLM so as to effectively perform patch matching. The main advantage of the proposed method is that it does not need to rotate patches in different orientations during patch matching. Experimental results show that the proposed method is superior to the traditional NLM, the state-of-the-art method Block-matching and 3-D (BM3D) filtering and the Unbiased NLM (UNLM) for MRI denoising.

关键词： Measurement Matched filters Three-dimensional displays Magnetic resonance imaging Noise reduction Superresolution Magnetic resonance Kernel Rotation measurement Image denoising

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Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning

arXiv

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arXiv 2025年

作者： Wang, Ziming Huang, Changwu Tang, Ke Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Data Science Lingnan University Hong Kong

Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness. This paper proposes a novel method to achieve procedural fairness during the model training phase. The effectiveness of the proposed method is validated through experiments conducted on one synthetic and six real-world datasets. Additionally, this work studies the relationship between procedural fairness and distributive fairness in ML models. On one hand, the impact of dataset bias and the procedural fairness of ML model on its distributive fairness is examined. The results highlight a significant influence of both dataset bias and procedural fairness on distributive fairness. On the other hand, the distinctions between optimizing procedural and distributive fairness metrics are analyzed. Experimental results demonstrate that optimizing procedural fairness metrics mitigates biases introduced or amplified by the decision-making process, thereby ensuring fairness in the decision-making process itself, as well as improving distributive fairness. In contrast, optimizing distributive fairness metrics encourages the ML model’s decision-making process to favor disadvantaged groups, counterbalancing the Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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MA-SAM: A Multi-Atlas Guided SAM Using Pseudo Mask Prompts Without Manual Annotation for Spine Image Segmentation

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2157-2169页

作者： Fan, Dingwei Zhao, Junyong Li, Chunlin Wang, Xinlong Zhang, Ronghan Zhu, Qi Wang, Mingliang Si, Haipeng Zhang, Daoqiang Sun, Liang Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Shandong University Cheeloo College of Medicine Department of Orthopedics Qilu Hospital Jinan250012 China Shandong Normal University College of Physics and Electronics Jinan250358 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China Shandong Qingdao266035 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Shenzhen Research Institute Nanjing211106 China

Accurate spine segmentation is crucial in clinical diagnosis and treatment of spine diseases. However, due to the complexity of spine anatomical structure, it has remained a challenging task to accurately segment spine images. Recently, the segment anything model (SAM) has achieved superior performance for image segmentation. However, generating high-quality points and boxes is still laborious for high-dimensional medical images. Meanwhile, an accurate mask is difficult to obtain. To address these issues, in this paper, we propose a multi-atlas guided SAM using multiple pseudo mask prompts for spine image segmentation, called MA-SAM. Specifically, we first design a multi-atlas prompt generation sub-network to obtain the anatomical structure prompts. More specifically, we use a network to obtain coarse mask of the input image. Then atlas label maps are registered to the coarse mask. Subsequently, a SAM-based segmentation sub-network is used to segment images. Specifically, we first utilize adapters to fine-tune the image encoder. Meanwhile, we use a prompt encoder to learn the anatomical structure prior knowledge from the multi-atlas prompts. Finally, a mask decoder is used to fuse the image and prompt features to obtain the segmentation results. Moreover, to boost the segmentation performance, different scale features from the prompt encoder are concatenated to the Upsample Block in the mask decoder. We validate our MA-SAM on the two spine segmentation tasks, including spine anatomical structure segmentation with CT images and lumbosacral plexus segmentation with MR images. Experiment results suggest that our method achieves better segmentation performance than SAM with points, boxes, and mask prompts. © 1982-2012 IEEE.

关键词： Image segmentation

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Event-based Video Reconstruction via Spatial-Temporal Heterogeneous Spiking Neural Network

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yu, Jiajie Lu, Xing Guo, Lijun Wang, Chong Li, Guoqi Qian, Jiangbo Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Nottingham Ningbo China Ningbo315100 China Ningbo University Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory Ningbo315211 China Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Beijing100045 China

Event cameras detect per-pixel brightness changes and output asynchronous event streams with high temporal resolution, high dynamic range, and low latency. However, the unstructured nature of event streams means that humans cannot analyze and interpret them in the same way as natural images. Event-based video reconstruction is a widely used method aimed at reconstructing intuitive videos from event streams. Most reconstruction methods based on traditional artificial neural networks (ANNs) have high energy consumption, which counteracts the low-power advantage of event cameras. Spiking neural networks (SNNs) are a new generation of event-driven neural networks that encode information via discrete spikes, which leads to greater computational efficiency. Previous methods based on SNNs overlooked the asynchronous nature of event streams, leading to reconstructions that suffer from artifacts, flickering, low contrast, etc. In this work, we analyze event streams and spiking neurons and explain poor reconstruction quality. We specifically propose a novel spatial-temporal heterogeneous (STH) spiking neuron suitable for reconstructing asynchronous event streams. The STH neuron adjusts the membrane decay coefficient adaptively and has better spatiotemporal perception. In addition, we propose a temporal-frequency calibration module (TFCM) based on the Fourier transform to improve the contrast of the reconstructions. On the basis of the above proposed neuron and module, we construct two SNN-based models, referred to as the STHSNN and TFCSNN. The goal of the former is to reduce the artifacts and flickering in reconstructions, whereas the latter focuses on enhancing the contrast. The experimental results demonstrate that our models can yield reconstructions in various scenarios, achieving better quality and lower energy consumption than previous SNNs. Specifically, the TFCSNN and STHSNN achieve top-2 performance among the SNN-based models, with energy consumption reductions of 3.

关键词： Image reconstruction

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Bi-Directional Deep Contextual Video Compression

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IEEE Transactions on Multimedia 2025年

作者： Sheng, Xihua Li, Li Liu, Dong Wang, Shiqi University of Science and Technology of China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition Hefei230027 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Deep video compression has made impressive process in recent years, with the majority of advancements concentrated on P-frame coding. Although efforts to enhance B-frame coding are ongoing, their compression performance is still far behind that of traditional bi-directional video codecs. In this paper, we introduce a bi-directional deep contextual video compression scheme tailored for B-frames, termed DCVC-B, to improve the compression performance of deep B-frame coding. Our scheme mainly has three key innovations. First, we develop a bi-directional motion difference context propagation method for effective motion difference coding, which significantly reduces the bit cost of bi-directional motions. Second, we propose a bi-directional contextual compression model and a corresponding bi-directional temporal entropy model, to make better use of the multi-scale temporal contexts. Third, we propose a hierarchical quality structure-based training strategy, leading to an effective bit allocation across large groups of pictures (GOP). Experimental results show that our DCVC-B achieves an average reduction of 26.6% in BD-Rate compared to the reference software for H.265/HEVC under random access conditions. Remarkably, it surpasses the performance of the H.266/VVC reference software on certain test datasets under the same configuration. We anticipate our work can provide valuable insights and bring up deep B-frame coding to the next level. © 2025 IEEE.

关键词： Software testing

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Emotion Recognition by Learning the Manifold of Fused Multiscale Information of EEG Signals

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IEEE Transactions on Affective Computing 2025年

作者： Li, Cunbo Zhang, Shuhan Mu, Yufeng Yang, Lei Peng, Yueheng Li, Fali Zhang, Yangsong Liang, Zhen Cao, Zehong Wan, Feng Yao, Dezhong Li, Peiyang Xu, Peng University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu610054 China University of Macau Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China Shenzhen University School of Biomedical Engineering Medical School Shenzhen518060 China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China University of South Australia STEM Mawson Lakes Campus AdelaideSA5095 Australia School of Computer Science University of Technology Sydney Australia University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Chongqing University of Posts and Telecommunications School of Bioinfomatics Chongqing400065 China

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems. © 2010-2012 IEEE.

关键词： Information fusion

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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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From Perception to Action:brain-to-brain Information Transmission of Pigeons

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Journal of Bionic Engineering 2024年第6期21卷 2913-2923页

作者： Lifang Yang Long Yang Haofeng Wang Mengmeng Li Zhigang Shang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China

Along with the flourishing of brain-computer interface technology,the brain-to-brain information transmission between different organisms has received high attention in recent ***,specific information transmission mode and implementation technology need to be further *** this paper,we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding *** system consists of three parts:(1)the“perception pigeon”learns to distinguish different visual stimuli with two discrepant frequencies,(2)the computer decodes the stimuli based on the neural signals recorded from the“perception pigeon”through a frequency identification algorithm(neural information decoding)and encodes them into different kinds of electrical pulses,(3)the“action pigeon”receives the Intracortical Microstimulation(ICMS)and executes corresponding key-pecking actions through discriminative learning(electrical stimulation encoding).The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%.Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding,providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

关键词： Pigeon Neural Information Decoding Electrical Stimulation Encoding Intracortical Microstimulation brain-to-brain Information Transmission

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Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation

arXiv

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arXiv 2025年

作者： Ji, Deyi Zhao, Feng Lu, Hongtao Wu, Feng Ye, Jieping MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Alibaba Group China

Low-level texture feature/knowledge is also of vital importance for characterizing the local structural pattern and global statistical properties, such as boundary, smoothness, regularity, and color contrast, which may not be well addressed by high-level deep features. In this paper, we aim to re-emphasize the low-level texture information in deep networks for semantic segmentation and related knowledge distillation tasks. To this end, we take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation. Specifically, Contourlet Decomposition Module (CDM) is introduced to decompose the low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge, and Texture Intensity Equalization Module (TIEM) is designed to extract and enhance the statistical texture knowledge with the corresponding Quantization Congruence Loss (QDL). Moreover, we propose the Co-occurrence TIEM (C-TIEM) and generic segmentation frameworks, namely STLNet++ and U-SSNet, to enable existing segmentation networks to harvest the structural and statistical texture information more effectively. Extensive experimental results on three segmentation tasks demonstrate the effectiveness of the proposed methods and their state-of-the-art performance on seven popular benchmark datasets, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Generation and Characterization of a Human-Derived iPSC line from a female child with First-Episode of sporadic schizophrenia

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Stem Cell Research 2025年 86卷

作者： Jiang, Youhui Zhou, Chuqing Zhao, Jun Ren, Xinyi Wang, Qiang Ni, Peiyan Li, Tao The Mental Health Center and Psychiatric Laboratory West China Hospital Sichuan University Chengdu 610041 China Affiliated Mental Health Center & Hangzhou Seventh People's Hospital Zhejiang University School of Medicine Zhejiang Key Laboratory of Clinical and Basic Research on Psychiatric Disorders Zhejiang Engineering Research Center for Intelligent Diagnosis and Treatment of Mental Disorders Hangzhou 310013 China Nanhu Brain-computer Interface Institute Hangzhou 311100 China Key Laboratory of Medical Neurobiology Ministry of Education Frontier Science Centre for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Zhejiang Hangzhou China

Schizophrenia is a highly heritable neurodevelopmental disorder. In this study, peripheral blood mononuclear cells (PBMCs) were obtained from a female child diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by introducing the reprogramming factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The iPSC line was confirmed through karyotyping and the expression of key pluripotency markers. These cells demonstrated the ability to differentiate into all three germ layers in vivo. © 2025 The Authors

关键词： Child Induced pluripotent stem cells (iPSCs) Psychiatric disorders Schizophrenia (SCZ)

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