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International Conference on Signal Processing Proceedings (ICSP)

作者： Caiqing Jian Yongbin Qin Lihui Wang Chen Ye Xinyu Cheng GState Key Laboratory of Public Big Data College of Computer Science and Technology Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.

关键词： Instance segmentation Measurement Visualization Adhesives Shape Semantic segmentation Glands Signal processing algorithms Signal processing Market research

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Difference in Uptake Pattern Heterogeneity and Reproducibility of Radiomics Features Between FAPI and FDG Pet/Ct Imaging of Tumors

Difference in Uptake Pattern Heterogeneity and Reproducibili...

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IEEE International Symposium on Biomedical Imaging

作者： Tianmeng Xu Yanchao Huang Tingting Ang Shuilin Chu Peiyin Luo Shunfang Wang Yufeng Zhang Lijun Lu Ganghua Tang Wenbing Lv School of Information and Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Department of Nuclear Medicine Laboratory for Quality Control and Evaluation of Radiopharmaceuticals Nanfang Hospital Southern Medical University Guangzhou China School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

To investigate the difference in uptake pattern heterogeneity between fluorodeoxyglucose (FDG) and fibroblast activation protein inhibitor (FAPI) PET/CT and the reproducibility of radiomics features with respect to different calculation methods of standardized uptake value (SUV). 210 patients with various tumors were involved. Among the extracted 497 radiomics features, there are only 20 features that exhibited an ICC difference of more than 0.25 between the two tracers when using different SUV calculation methods, revealing the similar influence of SUV calculation methods. In contrast, the ICC values for most texture features were generally lower (0.22-0.39) between FDG and FAPI PET images, indicating a large difference in uptake pattern heterogeneity between the two tracers. Our results demonstrated that the influence of SUV calculation methods on the reproducibility of FDG and FAPI PET features is similar. The uptake pattern heterogeneity characterized by radiomics features between FDG and FAPI PET images is significantly different.

关键词： Proteins Protein engineering Inhibitors Fibroblasts Feature extraction Reproducibility of results Radiomics

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A Lightweight Federated Learning Framework in a One-way Active Communication Environment

A Lightweight Federated Learning Framework in a One-way Acti...

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Frontiers of Computer Science and Information Technology (FCSIT), Euro-Asia Conference on

作者： Wei Li Xiang Cheng Key Laboratory of Intelligent Computing in Medical Image Shenyang China Computer Science and Engineering Northeastern University Shenyang China

In the medical field, patient data is protected by privacy and cannot be made public. Meanwhile, the medical institution is in the local area network, and the outside world cannot directly access it. In this case, data is not available, nor can a centralized machine learning approach be used to benefit from the data. Federated learning is a distributed machine learning method that solves the problem that data cannot be centralized for training like traditional machine learning because data is scattered and limited in access. This paper proposes a federated learning framework that enables decentralized machine learning. In the framework, we use a one-way active communication method that is not adopted by the current mainstream federated learning frameworks. The framework only allows clients to actively access the server, and multiple clients access the server asynchronously. The design of this communication method ensures the data security of the client in the framework. The framework proposed in this paper is flexible and lightweight. It does not depend on complex configuration and environment, and has strong scalability. Finally, the proposed framework is validated on federated learning tasks, proving the usability and effectiveness of the framework.

关键词： Training Federated learning Hospitals Scalability Distributed databases Servers Usability

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Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation

arXiv

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arXiv 2023年

作者： Zheng, Xu Zhu, Jinjing Liu, Yexin Cao, Zidong Fu, Chong Wang, Lin Hong Kong Northeastern University United States Dept. of Cse Hkust Hong Kong Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Neu China

The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular projection (ERP) and a lack of pixel-wise annotations. For this reason, some works treat the ERP and pinhole images equally and transfer knowledge from the pinhole to ERP images via unsupervised domain adaptation (UDA). However, they fail to handle the domain gaps caused by: 1) the inherent differences between camera sensors and captured scenes;2) the distinct image formats (e.g., ERP and pinhole images). In this paper, we propose a novel yet flexible dual-path UDA framework, DPPASS, taking ERP and tangent projection (TP) images as inputs. To reduce the domain gaps, we propose cross-projection and intra-projection training. The cross-projection training includes tangent-wise feature contrastive training and prediction consistency training. That is, the former formulates the features with the same projection locations as positive examples and vice versa, for the models' awareness of distortion, while the latter ensures the consistency of cross-model predictions between the ERP and TP. Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively. Importantly, the TP path can be freely removed after training, leading to no additional inference cost. Extensive experiments on two benchmarks show that our DPPASS achieves +1.06% mIoU increment than the state-of-the-art approaches. https://***/cvpr23/DPPASS. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

An AoI-Guaranteed Sensor Data Collection Strategy for RIS-as...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Yang, Bowen Yu, Yao Li, Jianqi Xu, Tongze Deng, Der-Jiunn Chin, Hui-Hsin Li, Yonghui Northeastern University School of Computer Science and Engineering Shenyang China National Changhua University of Education Department of Computer Science and Information Engineering China Overseas Chinese University Department of Information Technology China University of Sydney School of Electrical and Information Engineering Sydney Australia Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9798350345384

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data's AoI due to its high maneuverability. However, the UAV's high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Maneuverability

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Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning 9th

Towards Time-Variant-Aware Link Prediction in Dynamic Graph...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wen, Guangqi Cao, Peng Jin, Zhiyong Song, Ruoxian Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Dynamic graph link prediction is a challenging problem because the graph topology and node attributes vary at different times. A purely supervised learning scheme for the dynamic graph data usually leads to poor generalization due to insufficient supervision. As a promising solution, self-supervised learning can be introduced to dynamic graph analysis tasks. However, the self-supervised learning paradigm for dynamic graph learning has not been sufficiently investigated due to the complicated properties of the evolving graphs. We assume that the dynamic graphs consist of three independent types of key factors, i.e., graph time-variant information, time-invariant information and noise. Based on this assumption, we propose the Self-supervised Decoupling for Dynamic Graph (SDDG) framework for explicitly decoupling the representation which characterizes these three factors, thus enhancing the interpretability of the learned representation and link prediction performance. More specifically, we design an encoder-decoder architecture to sufficiently exploit the dynamic graph itself with multiple regularizations, so that the time-variant embedding of dynamic graph data can be effectively decoupled from the perspectives of node and structure for time-variant-aware link prediction. Experiments on five benchmark link prediction tasks demonstrate the significant improvement of our SDDG over the state-of-the-art methods. For example, SDDG achieves 98.2% and 97.3% Top-1 AUC on Reddit-B and Reddit-T, outperforming the DDGCL model, by 1.9% and 1.8%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Supervised learning

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An Adaptive Federated Domain Generalization Framework for Consumer Electronics Manufacturing Equipment Cross-Factory Fault Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Haodong Wang, Xingwei Li, Ying Yi, Bo Cao, Peng Huang, Min Li, Keqin Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University College of Information Science and Engineering Shenyang China Hunan University College of Computer Science and Electronic Engineering Changsha China

Reliability and operational efficiency of equipment are crucial in the manufacturing of consumer electronics. Existing fault detection methods often face limitations such as dataset dependence, poor scenario generalization, and data privacy issues when addressing the complex and diverse operating conditions in product manufacturing. To address these issues, this paper proposes a cross-factory fault detection framework for consumer electronics production equipment based on adaptive federated domain generalization. This framework reconsiders the limitations of Sharpness-Aware Minimization(SAM) and, by jointly considering local personalization and global generalization objectives, designs an adaptive weighting scheme to balance the trade-off between loss minimization and sharpness during optimization, thereby improving the model's robustness and accuracy under various working conditions. Then, A parameter momentum aggregation scheme is proposed on the server side to incorporate historical gradient information, reducing client drift impact and improving model convergence and stability. Finally, extensive scenario experiments were conducted on two public datasets. The results indicate that the proposed framework achieves an average improvement of 22.5% in fault detection accuracy over the baseline model across varying operating conditions and data distribution scenarios, demonstrating its effectiveness in addressing the challenges of complex condition variations and data privacy in consumer electronics manufacturing. © 1975-2011 IEEE.

关键词： Smart manufacturing

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Robust and faster zeroth-order minimax optimization: complexity and applications 24

Robust and faster zeroth-order minimax optimization: complex...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Weixin An Yuanyuan Liu Fanhua Shang Hongying Liu Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China and Peng Cheng Lab Shenzhen China

ISBN: (纸本)9798331314385

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dε-6) to find an ε-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations.

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Acoustic pressure change inside blood vessel exposed to high-intensity focused ultrasound: a simulation study

Acoustic pressure change inside blood vessel exposed to high...

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2024 International Conference on Future of Medicine and Biological Information Engineering, MBIE 2024

作者： Ri, Jonghyok Shang, Longzhong Pang, Na Xu, Lisheng Ji, Ning Yue, Xiangji Kim, Insong Zheng, Dingchang College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Mechanical Engineering and Automation Northeastern University Shenyang110169 China Postdoctoral Mobile Station for Basic Medicine Hebei Medical University Hebei Shijiazhuang050017 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang110169 China Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang110169 China School of Metallurgy Northeastern University Shenyang110819 China Research Centre of Intelligent Healthcare Coventry University Coventry United Kingdom

ISBN: (纸本)9781510682825

For the implementation of sonothrombolysis, the acoustic pressure inside the blood vessel should be revealed according to the ultrasound burst and biological tissue conditions. The objective of this study is to measure the magnitude of the acoustic pressure inside blood vessel exposed to high-intensity focused ultrasound (HIFU), and to reveal its changing characteristics according to the ultrasound parameters (power and frequency) and tissue configurations. The tissue mimicking phantom with HIFU exposure was modeled to simulate the acoustic pressure. The results showed that for a biological tissue system composed of skin, fat, muscle, and blood, the peak pressure at the focus with blood zone increased as the insonation frequency increased (0.5-2 MHz). Pressure attenuation with respect to blood vessel depth (10-30 mm) intensified according to increment of HIFU power and frequency. Greater attenuation was observed when the frequency surpassed 1.1 MHz, varying with skin (1-5 mm) and fat tissue (2-7 mm) thicknesses. The results suggest that at frequencies below 1.1 MHz, identical HIFU power can be utilized for different individuals and lesions, thereby achieving similar outcomes in clinical treatment. © 2024 SPIE.

关键词： Disease control

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