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Modeling and experimental verification of the rapid inversion model of the living body based on the fem model

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Recent Patents on Engineering 2021年第3期15卷 287-293页

作者： Zhang, Shuang Liu, Yi He Data Recovery Key Laboratory of Sichuan Province College of Computer Science & AI Neijiang Normal University Neijiang Sichuan China School of Life Science and Technology University of Electronic Science and Technology of P.R.China Chengdu Sichuan China High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan Chengdu Sichuan China Key Laboratory for Neuro Information of Ministry of Education Chengdu Sichuan China

Objective: This study aims to verify the practicability and accuracy of a finite element model (FEM) built using digital human model construction and to apply the model to actual explo-sion impact inversion. Methods: We proposed a simplified rapid inversion model using digital human modeling. In mod-eling, cell mesh generation was performed using preprocessing software (ANSYS ICEM CFD), measured organism data were used as the corresponding geometrical parameters of all specified parts, and material characteristics parameters were set according to tissue characteristics. FEM construction was completed in the ANSYS LS-DYNA software and verified by an equivalent experiment. Results: The rapid inversion FEM was built by employing digital human model construction. Through analysis, it was concluded that the calculation result was practically consistent with the experimental result. Conclusion: By creating the digital human model based on a CT dataset, the FEM inversion model of an approximately actual material environment can be rebuilt, and the model can be used to study human thorax injury characteristics. © 2021 Bentham science Publishers.

关键词： Mesh generation

Corrigendum to "brain network communication in remission: a comparative study of bipolar and unipolar depression" [J. Psychiatr. Res. 186 (2025) 1-8]

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Journal of psychiatric research 2025年

作者： Haiyan Liu Yi Xia Lingling Hua Hao Sun Rui Yan Zhijian Yao Jiaolong Qin Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China. Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Nanjing China. Electronic address: zjyao@***. The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China.

Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

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arXiv 2022年

作者： Li, Heng Liu, Haofeng Fu, Huazhu Shu, Hai Zhao, Yitian Luo, Xiaoling Hu, Yan Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China IHPC A*STAR Singapore Department of Biostatistics School of Global Public Health New York University New York United States Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Shenzhen People's Hospital Shenzhen China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. The degradation prevents reliable diagnosis by ophthalmologists or computer-aided systems. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images. Unfortunately, challenges remain in the deployment of these algorithms, such as collecting sufficient training data and preserving retinal structures. In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure. A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. Then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced. The experiments demonstrate the effectiveness of SCR-Net in the comparison with state-of-the-art methods and the follow-up clinical applications. The code is available at https://***/liamheng/ArcNet-Medical-Image-Enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

Effective and efficient multitask learning for brain tumor segmentation

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Effective and efficient multitask learning for brain tumor s...

作者： Cheng, Guohua Cheng, Jingliang Luo, Mengyan He, Linyang Tian, Yan Wang, Ruili Institute of Science and Technology for Brain-Inspired Intelligence Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Magnetic Resonance Imaging The First Affiliated Hospital of Zhengzhou University Zhengzhou450052 China Jianpei Technology Co. Ltd Hangzhou310018 China School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310018 China

Recently, brain tumor segmentation has achieved great success, partially because of deep learning-based relation exploration and multiscale analysis. However, the computational complexity hinders the real-time application. In this paper, we propose a revised multitask learning approach in which a lightweight network with only two scales is adopted to segment different kinds of tumor regions. Moreover, we design a hybrid hard sampling method that considers both sample sparsity and effectiveness. Extensive experiments on the BraTS19 segmentation challenge dataset have shown that our proposed method improves the Dice coefficient by a margin of 0.4–1.0 for different kinds of brain tumor regions and obtains results that are competitive with state-of-the-art brain tumor segmentation approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image segmentation

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

Graph jigsaw learning for cartoon face recognition

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arXiv 2021年

作者： Li, Yong Lao, Lingjie Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognize cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. © 2021, CC BY-NC-SA.

关键词： Face recognition

Learning fair face representation with progressive cross transformer

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arXiv 2021年

作者： Li, Yong Sun, Yufei Cui, Zhen Shan, Shiguang Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still challenges the practical face recognition system. The race factor has been proven to be a dilemma for fair FR (FFR) as the subject-related specific attributes induce the classification bias whilst carrying some useful cues for FR. To mitigate racial bias and meantime preserve robust FR, we abstract face identityrelated representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition. Originating from the signal decomposition theory, we attempt to decouple face representation into i) identity-related components and ii) noisy/identity-unrelated components induced by race. As an extension of signal subspace decomposition, we formulate face decoupling as a generalized functional expression model to cross-predict face identity and race information. The face expression model is further concretized by designing dual cross-transformers to distill identity-related components and suppress racial noises. In order to refine face representation, we take a progressive face decoupling way to learn identity/racespecific transformations, so that identity-unrelated components induced by race could be better disentangled. We evaluate the proposed PCT on the public fair face recognition benchmarks (BFW, RFW) and verify that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance. Besides, visualization results also show that the attention maps in PCT can well reveal the race-related/biased facial regions. © 2021, CC BY-NC-SA.

关键词： Face recognition

Meta auxiliary learning for facial action unit detection

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arXiv 2021年

作者： Li, Yong Shan, Shiguang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Cas Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Cas Center for Excellence in Brain Science and Intelligence Technology

Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two highly correlated tasks, and facial expression (FE) is relatively easy to annotate, we consider learning AU detection and FER in a multi-task manner. However, the performance of the AU detection task cannot be always enhanced due to the negative transfer in the multi-task scenario. To alleviate this issue, we propose a Meta Auxiliary Learning method (MAL) that automatically selects highly related FE samples by learning adaptative weights for the training FE samples in a meta learning manner. The learned sample weights alleviate the negative transfer from two aspects: 1) balance the loss of each task automatically, and 2) suppress the weights of FE samples that have large uncertainties. Experimental results on several popular AU datasets demonstrate MAL consistently improves the AU detection performance compared with the state-of-the-art multi-task and auxiliary learning methods. MAL automatically estimates adaptive weights for the auxiliary FE samples according to their semantic relevance with the primary AU detection task. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Evolutionary multi-objective optimization algorithm framework with three solution sets

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arXiv 2020年

作者： Ishibuchi, Hisao Pang, Lie Meng Shang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

It is assumed in the evolutionary multi-objective optimization (EMO) community that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm. The number of solutions to be presented to the decision maker can be totally different. In some cases, the decision maker may want to examine only a few representative solutions from which a final solution is selected. In other cases, a large number of non-dominated solutions may be needed to visualize the Pareto front. In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations with respect to the required number of solutions. The three solution sets are the main population of an EMO algorithm, an external archive to store promising solutions, and a final solution set which is presented to the decision maker. The final solution set is selected from the archive. Thus the population size and the archive size can be arbitrarily specified as long as the archive size is not smaller than the required number of solutions. The final population is not necessarily to be a good solution set since it is not presented to the decision maker. Through computational experiments, we show the advantages of this framework over the standard final population and final archive frameworks. We also discuss how to select a final solution set and how to explain the reason for the selection, which is the first attempt towards an explainable EMO framework. © 2020, CC BY.

关键词： Decision making