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LGNet:Local and global representation learning for fast biomedical image segmentation

Journal of Innovative Optical Health Sciences 2023年第4期16卷 29-39页

作者： Guoping Xu Xuan Zhang Wentao Liao Shangbin Chen Xinglong Wu School of Computer Science&Engineering Hubei Key Laboratory of Intelligent RobotWuhan Institute of TechnologyWuhanHubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhanHubei 430074P.R.China

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many *** on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous ***,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global *** address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this ***,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global *** has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from *** method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and ***,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on ***,the inference speed attains at 172 frames per second with 224-224 input *** extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

关键词： CNNs transformers segmentation medical image contextual information

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Shifts in intrinsic neural timescale of hippocampus support the maturation of inhibitory control and working memory in youth

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Medicine in Novel technology and Devices 2024年第2期22卷 115-123页

作者： Debin Zeng Qiongling Li Deyu Li Shuyu Li Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science&Medical EngineeringBeihang UniversityBeijing100083China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing100875China Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal UniversityBeijing100875China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing100875China State Key Laboratory of Virtual Reaility technology and Systems Beihang UniversityBeijing100083China

The intrinsic neural timescale(INT)provides temporal windows in brain activity that process information of different durations,crucial for the integration and segregation of external inputs and ultimately shaping cognition and *** research has uncovered a pronounced INT hierarchy along the adult hippocampus's long***,the development of INT organization within the hippocampus—particularly the pattern of its hierarchical structure and its impact on cognitive development—has not been thoroughly investigated in ***,we discovered that the INT distribution in youth presents a distinct hierarchical structure along both posterioranterior and proximal-distal axes of the ***,this hierarchical structure correlates signifi-cantly with the first principal gradient of the hippocampal-cortical functional connectome and the thickness of hippocampal grey ***,we observed notable changes in the hippocampal INT landscape during youth,characterized by a general narrowing of timescales,alongside dedifferentiation along the hippocampal organizational *** maturational changes significantly link to improvements in inhibitory control and working memory ***,our findings reveal the developmental patterns of temporal integration and segregation hierarchies within hippocampus,and highlights the profound significance of INT as a neural underpinning that orchestrates cognitive growth.

关键词： Intrinsic neural timescale Hippocampus Development Inhibitory control and working memory

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Heterogeneous Effects of Eye-close and Eye-open on Electroencephalographic Microstates of Depressed Brain in Resting State

Heterogeneous Effects of Eye-close and Eye-open on Electroen...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cui, Kunbo Zhao, Mingqi Liu, Di He, Minxin Zhao, Qinglin Hu, Bin School of Information Science and Engineering Lanzhou University Lanzhou China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

ISBN: (纸本)9798350386226

Previous studies have shown that the resting-state electroencephalogram (EEG) of depressed patients exhibits abnormal dynamic activation of large-scale networks in microstate analysis. However, the problem of heterogeneous results in microstate features as physiological markers for depressive disorders has not been addressed. An important factor contributing to this problem is that previous studies have overlooked the effects of eye-opening and eye-closing on resting-state EEG microstates in depression. To address this gap, the present study proposes a new microstate delineation method to accurately identify EEG microstates in both open-eye and closed-eye resting states, and further analyzes the differential performance of the depressed group and the control group in the two states. We validated our method on a dataset containing 64-channel EEG data (55 cases in the depressed group and 55 cases in the control group). The results showed that the classical seven microstate topographies were present in both open-eye and closed-eye conditions, which may explain why other studies have overlooked the impact of eye state on resting-state microstates in depression. In addition, the microstate characteristics of depression were differentially expressed in the open-eye and closed-eye conditions, with the depressed group showing more significant results in the closed-eye condition. Overall, our study demonstrates that eye state affects depression microstates and provides new insights to address the problem of heterogeneity in the results of EEG microstate studies of depression. © 2024 IEEE.

关键词： Electroencephalography

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TagVibra: push the limit of RFID-based vibration sensing 9

TagVibra: push the limit of RFID-based vibration sensing

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Li, Jian Li, Ping Zhu, Biaokai Dong, Chong Gong, Chao Lv, Zhao Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China Anhui University of Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China Shanxi Police College Taiyuan030401 China

ISBN: (纸本)9798350331240

RFID-based mechanical vibration detection is considered a promising method for many Internet of Things (IoT) applications. However, existing methods are affected by ambient interference and leakage from the reader signature, which makes it difficult to obtain high-precision detection results. To overcome this limitation, this paper introduces TagVibra, a high-precision non-contact vibration sensing system based on RFID technology. By leveraging the nonlinear effect inside the tag and the modulation mechanism of RFID, TagVibra can achieve sub-millimeter level vibration detection accuracy. We implement TagVibra on our Universal Software Radio Peripheral (USRP) platform and evaluate its performance via experiments. The experimental results show that TagVibra can detect vibration with amplitude as low as 0.3mm, and the average estimation error below 0.7Hz when the vibration frequency is 100Hz. © 2023 IEEE.

关键词： Frequency estimation

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Design and Control for WLR-3P:A Hydraulic Wheel-Legged robot

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Cyborg and Bionic Systems 2023年第1期4卷 243-259页

作者： Xu Li Haoyang Yu Haibo Feng Songyuan Zhang Yili Fu State Key Laboratory of Robotics and System College of Mechatronics EngineeringHarbin Institute of TechnologyHarbin 150001China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Intelligent Robot Research Center Zhejiang LaboratoryHangzhou 311100China

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex *** hybrid wheel-legged robot(WLR-3P,prototype of the third-generation hydraulic wheel-legged robot)has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough *** this paper,3 design requirements are proposed to improve the mobility and environmental adaptability of the *** meet these 3 requirements,2 design principles for each requirement are put ***,for light weight and low inertia with high stiffness,3-dimensional printing technology and lightweight material are ***,the integrated hydraulically driven unit is used for high power density and fast response ***,the microhydraulic power unit achieves power autonomy,adopting the hoseless design to strengthen the reliability of the hydraulic *** is more,the control system including hierarchical distributed electrical system and control strategy is *** mobility and adaptability of WLR-3P are demonstrated with a series of ***,the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.

关键词： robot robot terrain

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MultimodalSleepNet: A Lightweight Neural Network Model for Sleep Staging based on Multimodal Physiological Signals

MultimodalSleepNet: A Lightweight Neural Network Model for S...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cui, Kunbo Zhao, Mingqi He, Minxin Liu, Di Zhao, Qinglin Hu, Bin School of Information Science and Engineering Lanzhou University Lanzhou China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Cas Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

ISBN: (纸本)9798350386226

With social development, the demand for automatic sleep quality assessment in wearable devices is increasing, especially as sleep quality is closely related to the diagnosis of psychiatric disorders. However, existing automatic sleep stage classification models are mostly designed for unimodal signals, with an emphasis on increasing parameter scale and model depth. As a result, it is challenging to meet the requirements for both lightweight models and high accuracy in wearable device-based automatic sleep staging tasks. To address this problem, this study introduces a novel lightweight model for sleep staging, MultimodalSleepNet, which is based on multi-modal physiological signals. Specifically, the model is designed to capture the temporal dynamics of physiological signals and the spatial interactions between multimodal signals. Additionally, an inflationary convolution mechanism is incorporated to accelerate temporal feature extraction. We validate the model using the publicly available Sleep-EDF-Expanded dataset. Compared to similar studies, our model achieves outstanding performance, with accuracies of 93.1% and 90.2% in the three-stage and five-stage sleep recognition tasks, respectively. Notably, the three-stage classification results show an 11.9% improvement in modal fusion accuracy compared to unimodal signals, with an 8.9% improvement in multiclass F1 score and a 20.8% increase in Cohen's kappa coefficient. In conclusion, our study offers a reference for the design of lightweight models for sleep staging and provides new insights into feature extraction and fusion of multimodal signals. © 2024 IEEE.

关键词： Sleep research

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance 17

Self-supervised fast reconstruction method for diffusion MRI...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wu, Yuxin Zhang, Jian Wang, Lihui Cao, Ying Wu, Linhai Wu, Yunjia Ye, Chen Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 France

ISBN: (纸本)9798350387384

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor. © 2024 IEEE.

关键词： Self-supervised learning

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Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition 4th

Mirror Contrastive Loss Based Sliding Window Transformer for...

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4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

ISBN: (纸本)9789819640003

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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A robot Path Planning Approach Incorporating Dynamic Window and PPO Algorithms

SSRN

引用

SSRN 2024年

作者： Zhu, Qinxin Wang, Xu Zhu, Yonghong Miao, Liuyang School of Mechanical Engineering Suzhou University of Science and Technology Suzhou China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou China Jiangsu Intelligent Coexisting-Cooperative-Cognitive Robot Engineering Technology Center Suzhou China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou China School of Mechanical and Electronic Engineering Jingdezhen Ceramic University Jingdezhen China

Aiming at the problem that the robot is limited by the state space when moving in complex environment, it cannot provide a reasonable speed model for practical planning. a new method combining Dynamic Window Approach (DWA) and Proximal Policy Optimization (PPO) is proposed. Firstly, the linear velocity and angular velocity of the robot are added to the dynamic window calculation by using the designed velocity sampling space. Secondly, the near-end search strategy in reinforcement learning is used to train fixed weight coefficients. In the simulation experiment, the traditional dynamic window algorithm is compared with the fusion algorithm proposed in this paper. Compared with the traditional dynamic window algorithm, the planning time of the fusion algorithm is reduced by 45.12%, the path is shortened by 23.04%, and the inflection point in the path is reduced by 47.37%. The experimental results show that the obstacle avoidance, smoothness and robustness of this method are much higher than those of the traditional methods in the complex and dynamic obstacle environment. At the same time, the fusion algorithm is also applicable to real robots and real environments. © 2024, The Authors. All rights reserved.

关键词： Motion planning

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