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arXiv 2021年

作者： Li, Yijiang Cai, Wentian Gao, Ying Li, Chengming Hu, Xiping School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model long-range dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic U-Net structure with lateral connections and achieve state-of-the-art performance on Synapse multi-organ segmentation, Medical Segmentation Decathlon (MSD) Brain, and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An Artificial intelligence-Based Ecological Index for Prognostic Evaluation of Colorectal Cancer

SSRN

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SSRN 2022年

作者： Chen, Qicong Cai, Ming Fan, Xinjuan Liu, Wenbin Fang, Gang Yao, Su Xu, Yao Li, Qian Zhao, Yingnan Zhao, Ke Liu, Zaiyi Chen, Zhihua Institute of Computing Science and Technology Guangzhou University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Pathology The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China

Background and Objective: In the tumor microenvironment (TME), the active interaction between tumor cells and immune cells is crucial for predicting the prognosis of colorectal cancer. This study proposed a Morisita-Horn ecological index to quantify the spatial associations between tumor cells and immune cells in the TME by artificial intelligence (AI)-based method from immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) *** and methods: We used deep learning technology and traditional computer segmentation methods to segment the tumor nuclei, immune nuclei, and stroma nuclei in tumor regions in IHC-stained WSIs. The Morisita-Horn ecological index (Mor-index) was used to evaluate the spatial associations between the tumor cells and immune cells in the TME of CRC patients. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of Mor-index for overall survival (OS).Results: Experiments were conducted on two data sets of 569 patients to demonstrate the efficacy of the method. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27–0.77, P = 0.0014) and validation cohort (0.21, 0.10–0.46, Conclusion: This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making. © 2022, The Authors. All rights reserved.

关键词： Tumors

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Adversarial refinement network for human motion prediction

arXiv

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arXiv 2020年

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2020, CC-BY.

关键词： Errors

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The distribution pattern of corneal volume in Chinese myopic patients from multiple centers

MedComm - Future Medicine

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MedComm - Future Medicine 2023年第2期2卷

作者： Tang, Changting Qin, Linyuan Wang, Wei Lu, Suqing Li, Yinan Fang, Ying Yu, Honghua Hu, Yijun Guangdong Eye Institute Department of Ophthalmology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China The Second School of Clinical Medicine Southern Medical University Guangzhou China Affiliated Hospital of Guilin Medical University Guilin China Department of Epidemiology and Health Statistics School of Public Health Guilin Medical University Guilin China Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Health Guilin China The First Affiliated Hospital of Guangzhou Medical University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Aier Institute of Refractive Surgery Refractive Surgery Center Guangzhou Aier Eye Hospital Guangzhou China

Corneal volume (CV) is a useful index for detecting forme fruste keratoconus from normal corneas. It can be used to evaluate the whole cornea, since it can measure corneal areas up to 10 mm in diameter. Thus, CV has become the clinicians' interest as a diagnostic tool of corneal ectatic disease and a measure of corneal integrity to determine suitability for refractive surgery. We conducted a cross-sectional study including 7893 myopic patients from five ophthalmic centers to investigate the distribution pattern of CV. Our study showed that distribution of CV-3, CV-5, and CV-7 mm were slightly positively skewed and the 2.5th to 97.5th percentiles were 3.6–4.4, 10.4–12.8, 22.5–27.5 mm³, respectively. Central corneal thickness (CCT) was significantly correlated with CV in all measurement regions. The correlation between CV and CCT showed an inconsistent trend with the increase of age. The correlation coefficient between CV and CCT did not change significantly with the increase of myopia degree in low to moderate myopia, but fluctuated significantly in high myopia (less than −6.0 diopters). According to our results, corneal volume follows a slightly positively skewed distribution pattern in myopic Chinese patients. The information is useful for screening refractive surgery candidates and assessing the risk of corneal refractive surgery. © 2023 The Authors. MedComm – Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： corneal biometrics corneal volume (CV) myopia ocular biometrics refractive surgery

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Parameter training methods for convolutional neural networks with adaptive adjustment method based on Caputo fractional-order differences

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Chaos, Solitons & Fractals 2025年 199卷

作者： Haiming Zhao Honggang Yang Jiejie Chen Ping Jiang Zhigang Zeng School of Computer and Information Engineering Hubei Normal University Huangshi 435000 China School of Automation Wuhan University of Technology Wuhan 430000 China School of Computer Hubei Polytechnic University Huangshi 435000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430000 China

As deep learning technologies continue to permeate various sectors, optimization algorithms have become increasingly crucial in neural network training. This paper introduces two adaptive momentum algorithms based on Grünwald–Letnikov and Caputo fractional-order differences—Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam)—to update parameters more flexibly by adjusting momentum information. Commencing from the definitions of fractional derivatives, we propose integrating fractional-order differences with gradient algorithms in convolutional neural networks (CNNs). These adaptive momentum algorithms, leveraging Grünwald–Letnikov and Caputo fractional-order differences, offer enhanced flexibility, thereby accelerating convergence. Our nonlinear parameter tuning method for CNNs demonstrates superior performance compared to traditional integer-order momentum algorithms and the standard Adam algorithm. Experimental results on the BraTS2021 dataset and CIFAR-100 dataset reveal that the proposed fractional-order optimization algorithms significantly outperform their integer-order counterparts in model optimization. They not only expedite convergence but also improve the accuracy of image recognition and segmentation.

关键词： Adaptive momentum algorithms Convolutional neural networks Deep learning Fractional-order differences

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The Design of Logic Gate Based on Triplex Structures 14th

The Design of Logic Gate Based on Triplex Structures

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Hu, Yingxin Wang, Zhiyu Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China College of Information Science and Technology Shijiazhuang Tiedao University ShijiazhuangHebei050043 China School of Computer Science and Technology Huazhong University of Science and Technology WuhanHubei430074 China Department of Pathology Renmin Hospital of Wuhan University WuhanHubei430060 China

ISBN: (纸本)9789811534140

Molecular carch hotspots in the area of DNA nanotechnology due to its inherent large storage capacity, high parallelism, and energy efficiency. The assembled structures in the existing computing units mainly adopt a double-helix structure. However, besides duplex, DNA is known to adopt other helical forms such as triplex form. The triplex structure has introduced a novel paradigm into the DNA engineer owing to their unique characteristic sequence, structural and assembly requirements. Based on the assembly and disassembly of the triplex structure, a YES logic gate was constructed via strand displacement reaction. The experiment results showed that GC content and stem length would influence the displacement reaction. Then AND logic gate was also established through cooperative effect by two DNA strands. The construction of the cascading circuit indicated triplex-based logic gate could be integrated into larger circuits and enrich the toolbox of the computing devices. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Logic gates

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Manipulability and Robustness Optimization of the Cable-Driven Redundant Soft Manipulator

Manipulability and Robustness Optimization of the Cable-Driv...

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IEEE International Conference on Robotics and Biomimetics

作者： Yi Shen Yang Hong Wei Zhou Ruochen Tai Ye Yuan Han Ding School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan People’s Republic of China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan People’s Republic of China State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan People’s Republic of China

ISBN: (纸本)9781665405362

Redundancy makes soft manipulators manage to complete a variety of tasks in complex environments. However, a pseudo-inverse kinematics controller for redundant soft manipulators simply maximizes the manipulability while ignoring some material constraints, e.g., actuation saturation and control noise. As manipulability is the indicator for the dexterity of motion in all directions of the redundant soft manipulators, this work proposes an optimization based controller, where the manipulability and robustness are optimized for a cable-driven redundant soft manipulator to adapt actuation saturation and control noise. Experiments are conducted to demonstrate the effectiveness of the proposed controller.

关键词： Conferences Biomimetics Redundancy Kinematics Manipulators Robustness Trajectory

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Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

arXiv

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arXiv 2020年

作者： Cui, Yuqi Wang, Huidong Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Science and Engineering Shandong University of Finance and Economics Jinan250014 China

Fuzzy c-means based clustering algorithms are frequently used for Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One rule is initialized from each cluster. However, most of these clustering algorithms are unsupervised, which waste valuable label information in the training data. This paper proposes a supervised enhanced soft subspace clustering (SESSC) algorithm, which considers simultaneously the within-cluster compactness, between-cluster separation, and label information in clustering. It can effectively deal with high-dimensional data, be used as a classifier alone, or be integrated into a TSK fuzzy classifier to further improve its performance. Experiments on nine UCI datasets from various application domains demonstrated that SESSC based initialization outperformed other clustering approaches, especially when the number of rules is small. Copyright © 2020, The Authors. All rights reserved.

关键词： Fuzzy clustering

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A Multi-Criteria Optimal Operation Framework for Renewable Energy Integrated Data Center Microgrid with Waste Heat Recovery

A Multi-Criteria Optimal Operation Framework for Renewable E...

引用

Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Yuanzheng Li Jingjing Huang Yun Liu Hao Wang Yongzhen Wang Xiaomeng Ai School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China School of Electric Power Engineering South China University of Technology Guangzhou China Monash University Melbourne VIC Australia Energy Internet Research Institute Tsinghua University Beijing China School of Electrical and Electronics Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728186337

The rapid development of data centers (DCs) leads to a huge challenge in their energy consumption and environmental impact. It is promising to establish data center microgrid (DCMG) with renewable energy generation and waste heat recovery system to address these issues. However, the efficient energy management of DCMG is to be pursued. In this paper, we propose a multi-criteria optimal operation framework for the DCMG, considering coordinated scheduling of energy supply and demand. In this framework, multiple criteria are comprehensively considered via a multi-criteria optimization (MCO) approach. Then, we adopt the augmented e-constraint algorithm to address the corresponding MCO problem. Afterwards, a balanced decision making method is further proposed to determine the optimal scheduling solution. Finally, case study and results analysis verify the effectiveness of proposed multi-criteria optimal operation framework for the DCMG.

关键词： Data centers Renewable energy sources Uncertainty Supply and demand Simulation Decision making Optimal scheduling

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research Square

引用

Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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