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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation

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arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. Copyright © 2024, The Authors. All rights reserved.

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Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection

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arXiv 2022年

作者： Wu, Jiaxi Chen, Jiaxin He, Mengzhe Wang, Yiru Li, Bo Ma, Bingqi Gan, Weihao Wu, Wei Wang, Yali Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Shenzhen Institutes of Advanced Technology Chinese Academy of Science China SenseTime Research Shanghai AI Laboratory China

Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. To address this issue, we propose a novel approach, namely target-relevant knowledge preservation (TRKP), to unsupervised multi-source DAOD. Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain. The teacher network is further equipped with an adversarial multi-source disentanglement (AMSD) module to preserve source domain-specific knowledge and simultaneously perform cross-domain alignment. Besides, a holistic target-relevant mining (HTRM) scheme is developed to re-weight the source images according to the source-target relevance. By this means, the teacher network is enforced to capture target-relevant knowledge, thus benefiting decreasing domain shift when mentoring object detection in the target domain. Extensive experiments are conducted on various widely used benchmarks with new state-of-the-art scores reported, highlighting the effectiveness. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

Parameter Optimization via Reinforcement Learning for the Regulation of Swarms

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Parameter Optimization via Reinforcement Learning for the Re...

International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Qizhen Wu Gaoxiang Liu Kexin Liu Lei Chen School of Automation Science and Electrical Engineering Beihang University Beijing China The State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space.

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Vulnerability Name Prediction Based on Enhanced Multi-Source Domain Adaptation

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Vulnerability Name Prediction Based on Enhanced Multi-Source...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Ying Xing Mengci Zhao Bin Yang Yuwei Zhang Wenjin Li Jiawei Gu Jun Yuan Lexi Xu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing China Yunnan Key Laboratory of Software Engineering Yunnan China China Unicom Research Institute Beijing China School of Computer Science Peking University Beijing China NSFOCUS Technologies Group Co. Ltd. Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

关键词： Training software maintenance Java Data privacy Correlation Training data Maintenance engineering

Region Attention Transformer for Medical Image Restoration

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arXiv 2024年

作者： Yang, Zhiwen Chen, Haowei Qian, Ziniu Zhou, Yang Zhang, Hui Zhao, Dan Wei, Bingzheng Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Department of Gynecology Oncology National Cancer Center National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China ByteDance Inc. Beijing100098 China

Transformer-based methods have demonstrated impressive results in medical image restoration, attributed to the multi-head self-attention (MSA) mechanism in the spatial dimension. However, the majority of existing Transformers conduct attention within fixed and coarsely partitioned regions (e.g. the entire image or fixed patches), resulting in interference from irrelevant regions and fragmentation of continuous image content. To overcome these challenges, we introduce a novel Region Attention Transformer (RAT) that utilizes a region-based multi-head self-attention mechanism (R-MSA). The R-MSA dynamically partitions the input image into non-overlapping semantic regions using the robust Segment Anything Model (SAM) and then performs self-attention within these regions. This region partitioning is more flexible and interpretable, ensuring that only pixels from similar semantic regions complement each other, thereby eliminating interference from irrelevant regions. Moreover, we introduce a focal region loss to guide our model to adaptively focus on recovering high-difficulty regions. Extensive experiments demonstrate the effectiveness of RAT in various medical image restoration tasks, including PET image synthesis, CT image denoising, and pathological image super-resolution. Code is available at https://***/RAT. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

All-In-One Medical Image Restoration via Task-Adaptive Routing

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arXiv 2024年

作者： Yang, Zhiwen Chen, Haowei Qian, Ziniu Yi, Yang Zhang, Hui Zhao, Dan Wei, Bingzheng Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology Ministry of Education Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Department of Gynecology Oncology National Cancer Center National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China ByteDance Inc. Beijing100098 China

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due to significant differences between different MedIR tasks, training a universal model often encounters task interference issues, where different tasks with shared parameters may conflict with each other in the gradient update direction. This task interference leads to deviation of the model update direction from the optimal path, thereby affecting the model’s performance. To tackle this issue, we propose a task-adaptive routing strategy, allowing conflicting tasks to select different network paths in spatial and channel dimensions, thereby mitigating task interference. Experimental results demonstrate that our proposed All-in-one Medical Image Restoration (AMIR) network achieves state-of-the-art performance in three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis, both in single-task and all-in-one settings. The code and data will be available at https://***/Yaziwel/AMIR. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

research on Zero Trust Dynamic Access Control Method Based on Machine Learning

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SSRN

SSRN 2024年

作者： Wang, Xinyu Wang, Fei Xi, Xuefeng Cui, Zhiming School of Electronics and Information Engineering Suzhou University of Science and Technology Jiangsu Suzhou215000 China Key Laboratory for Safety-critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing211106 China Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology Jiangsu Suzhou215000 China Suzhou Smart City Research Institute Jiangsu Suzhou215000 China

With the rise of cloud computing, traditional access control methods lack intelligent adaptability and are unable to meet the flexible, dynamic and fine-grained security management needs of multi-tenant management systems in cloud environments. In response to this problem, this paper proposes a zero-trust dynamic access control method based on machine learning. Based on the zero-trust access control model, this method proposes a zero-trust access control model based on machine learning and calculates the Access the trust value of the subject to achieve real-time analysis of user behavior and dynamic adjustment of permissions. This paper takes the medical and health data platform as an example to verify the effectiveness of this method, and verifies the efficiency and performance of this method through evaluation experiments. Experimental results show that this method can provide an effective solution for the security management of multi-tenant management systems in cloud environments. Compared with traditional access control methods and zero-trust access control methods, this method can provide an effective solution. A more intelligent, dynamic and fine-grained data security protection mechanism. © 2024, The Authors. All rights reserved.

关键词： Access control

VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

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VS-Net: A Voxel Encoding and Sparse Convolution Embedded Net...

IEEE International Conference on Robotics and Biomimetics

作者： Meng Liu Jianwei Niu Yu Liu China North Artificial Intelligence and Innovation Research Institute Beijing China China North Vehicle Research Institute Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Software Development Envi-ronment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781665481106

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios.

关键词： Point cloud compression Laser radar Three-dimensional displays Convolution Object detection Detectors Feature extraction

3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis

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arXiv 2022年

作者： Zhou, Yang Yang, Zhiwen Zhang, Hui Chang, Eric I-Chao Fan, Yubo Xu, Yan School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Department of Biomedical Engineering Tsinghua University Beijing100084 China Microsoft Research Beijing100080 China

Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-dose. Previous studies based on deep learning methods usually directly extract hierarchical features for reconstruction. We notice that the importance of each feature is different and they should be weighted dissimilarly so that tiny information can be captured by the neural network. Furthermore, the synthesis on some regions of interest is important in some applications. Here we propose a novel segmentation guided style-based generative adversarial network (SGSGAN) for PET synthesis. (1) We put forward a style-based generator employing style modulation, which specifically controls the hierarchical features in the translation process, to generate images with more realistic textures. (2) We adopt a task-driven strategy that couples a segmentation task with a generative adversarial network (GAN) framework to improve the translation performance. Extensive experiments show the superiority of our overall framework in PET synthesis, especially on those regions of interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks