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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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TIDE: a Timing-Deterministic and Efficient Executor for micro-ROS

TIDE: a Timing-Deterministic and Efficient Executor for micr...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Zilong Wang Songran Liu Xu Jiang Dong Ji Yi Wang Key Laboratory of Intelligent Computing in Medical Image Northeastern University China

The micro-ROS is widely used to bridge the gap between resource-constrained microcontrollers and larger processors in ROS-based robotic applications. We found that there are some performance issues in micro-ROS because of the unreasonable design of the micro-ROS default RCLC Executor. To improve the timing predictability and run-time efficiency in micro-ROS, we have designed and implemented a new executor called the TIDE Executor, and conducted comprehensive experiments to evaluate the TIDE Executor. The experimental results demonstrate that our TIDE Executor exhibits superior performance compared to the default RCLC Executor.

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Adaptive delay-energy balanced partial offloading strategy in Mobile Edge computing networks

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Digital Communications and Networks 2023年第6期9卷 1310-1318页

作者： Shumei Liu Yao Yu Lei Guo Phee Lep Yeoh Branka Vucetic Yonghui Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyang110819China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Electrical and Information Engineering University of SydneySydneyNSW2006Australia

Mobile Edge computing(MEC)-based computation offloading is a promising application paradigm for serving large numbers of users with various delay and energy *** this paper,we propose a flexible MECbased requirement-adaptive partial offloading model to accommodate each user's specific preference regarding delay and energy *** address the dimensional differences between time and energy,we introduce two normalized parameters and then derive the computational overhead of processing *** from existing works,this paper considers practical variations in the user request patterns,and exploits a flexible partial offloading mode to minimize computation overheads subject to tolerable delay,task workload and power *** the resulting problem is non-convex,we decouple it into two convex subproblems and present an iterative algorithm to obtain a feasible offloading *** experiments show that our proposed scheme achieves a significant improvement in computation overheads compared with existing schemes.

关键词： Mobile edge computing(MEC) Delay Energy consumption Dynamic balance Partial computation offloading

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Indoor Multi-Anchor Collaborative Localization for Unmanned Systems

Indoor Multi-Anchor Collaborative Localization for Unmanned ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Tai, Beijia Wei, Yangjie Zhang, Guangyu He, Yuqing Cui, Siyao Ministry of Education College of Computer Science and Engineering Northeastern University Key Laboratory of Intelligent Computing in Medical Image Wenhua Street 3 Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781665481090

Accurate tracking of unmanned devices is essen-tial for higher-level decision-making in collaborative confrontation scenarios. However, the robustness of current positioning methods based on Ultra-Wideband (UWB) is limit, and the positioning error is unstable. This paper presents a collabo-rative localization algorithm specifically designed for indoor unmanned systems, which leverages UWB technology combined with Least Squares (LS) and Extended Kalman Filter (EKF) models to achieve anchor calibration, as well as precise and stable positioning of unmanned devices. First, the positions of anchors is estimated using fixed-point ranging information;Second, the positions of anchors are simultaneously corrected while locating the tags on unmanned devices. The experimental results show that our algorithm achieves positioning accuracy within 10 cm in the three-dimensional space, even with a ranging error of 5 cm. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Deep Learning-Based Prediction of Myelosuppression in Lymphoma Patients During Chemotherapy Using Multimodal Radiological images with Subcutaneous Adipose Tissue

Deep Learning-Based Prediction of Myelosuppression in Lympho...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Du, Tianming Sun, Hongzan Yang, Jinzhu Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Shengjing Hospital China Medical University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland

ISBN: (纸本)9789819708543

Lymphoma is a malignant tumor, and diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma. Due to its biological characteristics, surgical treatment is difficult. The main treatment for DLBCL is chemotherapy, with the R-CHOP regimen being the most common. The vast majority of patients require lifelong treatment. Myelosuppression during chemotherapy is the most common adverse reaction in DLBCL patients and directly affects the progress of chemotherapy. Accurately predicting whether patients need early intervention before chemotherapy can greatly improve their prognosis. In this paper, we propose a neural network that uses PET/CT images of subcutaneous adipose tissue before treatment to predict myelosuppression in DLBCL patients. The model achieves a classification accuracy of 93.57%. This indicates that the growth distribution pattern and metabolic characteristics of adipose tissue are important for DLBCL patients. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chemotherapy

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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Computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Computer-aided diagnosis of pneumonia based on deep learning is a research ***,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this *** main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale *** MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features ***,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature ***,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different *** verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried *** the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,*** the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,*** model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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Texture Features and Machine Learning Based Environmental Microorganism Microscopic image Classification

Texture Features and Machine Learning Based Environmental Mi...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Ouyang, Xinyu Yuan, Huaqian Zhou, Shangqi Fu, Yanyu Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

In recent years, with the continuous development of molecular biology technology, the effective use of environmental microorganisms to solve the problem of pollution in the ecological environment has been widely used. However, the original microbial cultivation, environmental application and monitoring have the disadvantages of high degree of specialization, long time and high cost. In this case, the use of computer image processing and machine learning techniques for microbial identification and classification becomes a more efficient, economical and necessary method. This paper presents an environmental microorganism microscopic image classification method based on texture features and machine learning. In the research and experiments, microscopic images of microorganisms are cropped, grayed, data augmented, target areas segmented and extracted four types of texture features. The training set and test set are divided in a 1:1 ratio, and different recognition results are obtained based on different texture feature vectors and classifier models. Ultimately, the gradient-gray co-occurrence matrix feature combined with the Random Forest algorithm classifier achieves an F1-measure value of nearly 40%, which improves the work efficiency of environmental microbial researchers and related field workers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microorganisms

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Skin Cancer image Identification Using Deep Convolutional Neural Networks

Skin Cancer Image Identification Using Deep Convolutional Ne...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1402-1411页

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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