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Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

Journal of beijing Institute of Technology 2023年第5期32卷 562-578页

作者： Yufan Liu Kongming Liang Yinuo Jing Shen Wang Zhanyu Ma Yiming Li Yizhou Yu Yizhou Wang Jun Guo Pattern Recognition and Intelligent System Laboratory School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing 100876China. Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China AI Lab Deepwise HealthcareBeijing 100089China

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain ***,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural *** overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline *** proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context *** exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical *** maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby *** experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

关键词： brain midline delineation refinement network structure prior connectivity regular loss

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Data cleaning method for the process of acid production with flue gas based on improved random forest

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Chinese Journal of Chemical Engineering 2023年第7期59卷 72-84页

作者： Xiaoli Li Minghua Liu Kang Wang Zhiqiang Liu Guihai Li Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital CommunityMinistry of EducationBeijing 100124China Guixi Smelter Jiangxi Copper Co.LtdGuixi 335400China Beijing RTlink Technology Co. LtdBeijing 100102China

Acid production with flue gas is a complex nonlinear process with multiple variables and strong *** operation data is an important basis for state monitoring,optimal control,and fault ***,the operating environment of acid production with flue gas is complex and there is much *** data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and ***,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is ***,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the ***,an improved random forest regression model is *** algorithm is used to optimize the hyperparameters of the random forest regression *** the optimal parameter combination is found in the search space and the trend of data is ***,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is *** show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue *** method improves the accuracy of compensation for missing *** the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature *** conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

关键词： Acid production Data cleaning Isolation forest Random forest Data compensation

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Towards IoT-based sustainable digital communities

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intelligent and Converged Networks 2022年第2期3卷 190-203页

作者： Fangyu Li Hongyan Yang Xuejin Gao Honggui Han Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemEngineering Research Center of Digital CommunityMinistry of Educationand Beijing Artificial Intelligence InstituteBeijing University of TechnologyBeijing 100124China

Seeking continuous development,a modern community must also be able to adapt to future possible challenges using constrained or limited *** a revolutionary communication paradigm,the Internet of Things(IoT)empowers the cutting-edge and emerging applications which enable manifold new intelligent services towards a smart *** sophisticated ecosystem of a digital community is made feasible by the IoT infrastructure,which also provides community control with access to a wealth of actual *** addition,IoT platforms empower the ubiquitous computing ability,providing more potentials to the actuators in perception layer in the IoT *** more and more population in the urban areas,sustainability issues have become a key factor to consider in the development of a digital *** give a modern survey in this study on the most recent developments in IoT for sustainable digital *** carefully examining the most recent literature,we specifically highlight the various smart digital community application scenarios,such as smart buildings,energy management,green transportation,trash management,*** also look into a number of major issues facing the use of IoT technology in digital ***,we discuss potential future applications and future research areas for IoT,the critical component of sustainable digital communities.

关键词： Internet of Things digital communities sustainability smart systems

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TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography 18th

TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Ex...

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18th Chinese Conference on Image and Graphics Technology and Application Conference, IGTA 2023

作者： Zhang, Wenqian Feng, Jinchao Li, Zhe Sun, Zhonghua Jia, Kebin Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9789819975488

Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of luminescent sources within tissue. However, reconstructing the distribution of the quantum field from boundary measurements is a typical ill-posed problem. In this work, we propose a novel two-step reconstruction network (TSR-Net) based on a fusion mechanism, that integrates two encoder-decoder networks (ED-Net) using a concatenation block. Firstly, an ED-Net is trained to learn the CT structural features of tissues with the measured data. Then, the trained ED-Net is fixed and cascaded by another ED-Net for a second-step training to predict the 3D distributions. Numerical simulations reveal that the proposed approach can not only accurately reconstruct the intensity values of the luminescent sources, but also achieve a reconstruction resolution of 1mm with low target-background contrast. Furthermore, the well-trained network is still effective in the reconstruction of tissues with different shapes, which indicates an excellent generalization ability of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Luminescence

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Improved image quality of diffusion optical tomography through a deep learning based post-processing method 16

Improved image quality of diffusion optical tomography throu...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Lin, Shumin Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9781510667341

Diffusion optical tomography (DOT) is an emerging optical imaging modality with the advantages of being low-cost, non-invasive and non-damage-free, it has demonstrated great potential in differentiating benign and malignant breast tumors. However, due to the strong scattering of biological tissues and the limited number of boundary measurements, the DOT image reconstruction is ill-posed and ill-conditioned. Conventional DOT image reconstruction algorithms such as Tikhonov regularization, are easy to implement. Nevertheless, images with poor quality were usually achieved. In this paper, to improve the image quality of DOT, we develop a post-processing reconstruction algorithm based on deep learning. It has a type of U-net architecture, but with the low-quality image obtained with Tikhonov regularization and the acquired multi-wavelength optical signals as the input. Meanwhile, the output of the network is images of chromophores, including HbO, Hb and water. To train the proposed algorithm, we construct a dataset with 15, 000 images for training, 6, 000 for validation, and 3, 900 for testing. Our results demonstrate that it has achieved good reconstruction results for the phantoms with single or double inclusions in terms of PSNR and SSIM. Compared to the Tikhonov regularization, the average PSNR of HbO, Hb and water are improved by 55%, 74% and 71%, respectively, and the average SSIM are improved by 11%, 76%, and 70%, respectively. © 2023 SPIE.

关键词： Image quality

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Limited-Angle cherenkov-excited luminescence scanned tomography reconstruction based on spatial attention module 16

Limited-Angle cherenkov-excited luminescence scanned tomogra...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Geng, Mengfan Zhang, Hu Zhang, Jingyue Jia, Kebin Sun, Zhonghua Li, Zhe Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9781510667341

Cherenkov-excited luminescence scanned tomography (CELST) is a new emerging imaging modality, which uses the Cherenkov light to excite fluorophores for tomographic imaging. In order to improve the imaging depth and spatial resolution, a rotational CELST was developed to scan the imaging object to produce sinogram data, and a filtered back projection (FBP) was used to recover the distribution of fluorophores. However, the images reconstructed by FBP are usually corrupted by artifacts due to measurements from limited angles. To reduce the artifacts, we propose a deep learning-based reconstruction algorithm (SAM-Unet), which is based on a fully convolutional deep neural network with U-Net structure, and a spatial attention module was added between the encoder and the decoder. The image features extracted by the spatial attention module are transferred to the decoder through a skip connection structure. The effectiveness of the proposed SAM-Unet is verified by numerical experiments, and the results show that the SAM-Unet can improve the mean square error (MSE) (97.5%), peak signal-To-noise ratio (PSNR) (81.9%) and structure similarity index measure (SSIM) (63.4%) compared with the FBP algorithm. Compared with the deep learning method U-Net, the MSE improved 39.8%, the PSNR improved 8.0% and SSIM improved 2.6%. © 2023 SPIE.

关键词： Image reconstruction

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A Self-supervised Learning Reconstruction Algorithm with an Encoder-Decoder Architecture for Diffuse Optical Tomography 18th

A Self-supervised Learning Reconstruction Algorithm with an ...

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18th Chinese Conference on Image and Graphics Technology and Application Conference, IGTA 2023

作者： Li, Yaxuan Wei, Chengpu Zhang, Wenqian Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9789819975488

Diffuse optical tomography (DOT) is an emerging non-invasive optical imaging technique, which has a promising application in breast cancer detection and diagnosis. However, the conventional image reconstruction algorithm in DOT is time-consuming and easy to error when recovering the distribution of optical parameters within the complete tissue. In this paper, we present an end-to-end reconstruction algorithm for DOT based on a deep convolutional encoder-decoder architecture, which consists of a data processing part and a convolutional encoder-decoder net. Its effectiveness was evaluated using simulation data. The results show that the overall quality of our method is significantly improved compared with the traditional algorithm based on the FEM method, the single inclusion deviation is reduced by 150% compared with the traditional algorithm, the standard deviation is reduced by 50%;multiple inclusions deviation is reduced by 100% and the standard deviation by 38.7%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image reconstruction

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Diffuse optical tomography image reconstruction based on graph convolution network 16

Diffuse optical tomography image reconstruction based on gra...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Su, Linxuan Wei, Chengpu Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (纸本)9781510667341

Diffuse optical tomography (DOT) is a promising noninvasive imaging method which quantifies optical parameters to achieve functional characteristics of biological tissues. Since boundary measurements are limited and the light propagation in biological tissues is highly diffusive, DOT image reconstruction is ill-posed and ill-conditioned. Traditional iterationbased regularization methods, such as Tikhonov regularization, tend to produce low-quality images with severe artifacts. Deep learning reconstruction methods based on convolutional neural networks may ignore the structure information of finite element mesh during feature extraction and cause the loss of spatial features. In order to overcome these problems, we propose a novel neural network based on graph convolution to improve the quality of reconstructed DOT images. Graph convolution can make full use of the spatial structure information of finite element mesh and deal with the potential correlation between optical features effectively. To verify the performance of the proposed algorithm, simulation experiments were performed. We also compared the proposed algorithm with Tikhonov regularization method and deep learning method based on convolutional neural network. Experimental results show that proposed algorithm obtains better reconstruction results in terms of ABE, MSE, PSNR and SSIM. © 2023 SPIE.

关键词： Convolution

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Event-Based Approximate Neuro-Optimal Tracking Control Design Involving a Wastewater Treatment Application 41

Event-Based Approximate Neuro-Optimal Tracking Control Desig...

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第41届中国控制会议

作者： Jiangyu Wang Ding Wang Lingzhi Hu Mingming Zhao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Laboratory of Smart Environmental Protection Beijing Institute of Artificial Intelligence

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Water resources shortage can be availably relieved by the wastewater *** this paper,we develop a novel event-based near-optimal tracking control algorithm for a class of unknown dynamic nonaffine systems,with the purpose of improving the control performance of the dissolved oxygen concentration and the nitrate nitrogen concentration in the nonlinear wastewater treatment ***,the classical heuristic dynamic programming(HDP) method is established to solve the nearoptimal tracking control ***,the event-triggered mechanism is introduced to reduce the communication burden in the traditional tracking control ***,the method can ensure that the closed-loop system has an acceptable performance in *** addition,we provide three kinds of neural networks for the implementation of the event-based HDP ***,a wastewater treatment application is chosen to testify the superior performance of the proposed approach.

关键词： Adaptive critic design event-triggered control neural networks nonaffine nonlinear systems optimal tracking control

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Wafer map defect patterns classification based on a lightweight network and data augmentation

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CAAI Transactions on intelligence Technology 2023年第3期8卷 1029-1042页

作者： Naigong Yu Huaisheng Chen Qiao Xu Mohammad Mehedi Hasan Ouattara Sie Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System BeijingChina Engineering Research Center of Digital Community Ministry of EducationBeijingChina College of Robotic UniversitéFélix Houphouët‐BoignyAbidjanCôte d'Ivoire

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production *** the wafer testing stage,deep learning methods are widely used in wafer defect detection due to their powerful feature extraction ***,most of the current wafer defect patterns classification models have high complexity and slow detection speed,which are difficult to apply in the actual wafer production *** addition,there is a data imbalance in the wafer dataset that seriously affects the training results of the *** reduce the complexity of the deep model without affecting the wafer feature expression,this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet *** addition,to reduce the impact of data imbalance on model training,this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden *** method proposed in this paper has an average accuracy of 95.4%on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs,and takes only 22.99 s to detect 1000 wafer *** with the original PeleeNet network without optimization,the number of parameters and FLOPs are reduced by 92.68%and 58.85%,*** augmentation on the minority class wafer map improves the average classification accuracy by 1.8%on the WM‐811K *** the same time,the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

关键词： convolutional autoencoder lightweight network wafer defect detection

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