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13th International Conference on Health Information Science, HIS 2024

作者： Li, Qingya Yuan, Ye Liu, Lu Zhang, Ziming Tan, Wenjun College of Computer Science and Engineering Northeastern University Shenyang110189 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China

ISBN: (纸本)9789819655960

In medical imaging, especially CT scanning, accurate classification of arteries and veins is crucial for diagnosis and treatment. Existing deep learning methods, although capable of capturing arterial and venous features, often process voxel points independently and lack a holistic understanding of vascular branch structures. This limitation results in misclassifications at branch points, especially in distal branches, necessitating post-processing for correction. Traditional post-processing algorithms, such as arterial and venous density calculations or graph cut methods, can correct short intrusions but are limited in addressing long intrusions or mixed intrusions, hindering the application in complex vascular networks. To address this issue, this paper proposes V-GnNet, a fusion learning method combining voxel-based predictions and graph structure node predictions. Firstly, V-GnNet employs an iterative 3D neural network based on the UNet architecture, IterUNet3D, for preliminary classification of vascular data. IterUNet3D enhances the classification performance of arteries and veins by enriching the network's multi-level feature inputs through iterative mini-UNet3D modules. Subsequently, a special graph structure is established by extracting the vascular skeleton, integrating priori knowledge into a feature matrix, and utilizing a Graph Attention Network (GAT) for node classification of the IterUNet3D results. Finally, a voting algorithm fuses the voxel prediction results and node prediction results, ensuring consistent branch classification in artery and vein separation, therefore addressing the challenges of branch misclassification. Experimental results demonstrate that V-GnNet significantly improves the accuracy and consistency of pulmonary artery and vein classification, effectively reducing branch misjudgments and mutual intrusions, which showcases its great potential in medical image processing. © The Author(s), under exclusive license to Springer Nature S

关键词： Deep learning

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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A segmentation method of airway from chest CT image based on VGG-Unet neural network

A segmentation method of airway from chest CT image based on...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shao, Wei Wang, Zhe Zhang, Ziming Zhou, Qinghua Wang, Ruoyu Tan, Wenjun Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Affiliated Zhongshan Hospital of Dalian University Oncology Department Dalian116001 China

ISBN: (纸本)9781665468190

Segmenting the pulmonary airway in CT images to obtain its anatomical structure information can help doctors better understand the patient's condition and determine the treatment plan. However, due to the blurred boundary of the pulmonary airway in CT images, it has a high degree of overlap with the surrounding pulmonary parenchyma and other tissues, making segmentation difficult. In this paper, a method based on the separation of pulmonary airway in CT images is investigated. Based on the original U-net network, using the idea of migration learning, the U-net and VGG16 network with partially similar network structure are fused to form a VGG-Unet joint neural network, which not only accelerates the model training and image segmentation efficiency, but also ensures the accuracy of image segmentation. The experimental data source used CT images acquired from the partner hospital. The algorithm in this paper was used to segment and extract the CT images of airway of lungs with an accuracy of 86.52%, and the segmentation efficiency was high and stable. © 2022 IEEE.

关键词： image segmentation

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An Adaptive Speech Noise Reduction Method Based on Noise Classification

An Adaptive Speech Noise Reduction Method Based on Noise Cla...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Ben Niu Yangjie Wei Key Laboratory of Intelligent Computing in Medical Image Ministry of Education College of Computer Science and Engineering Northeastern University Shenyang

To improve the accuracy and robustness of existing speech denoising methods, this study proposes an adaptive speech noise reduction method based on noise classification. First, to adapt to different time-varying characteristics of various noises in complex environments, a new acoustic feature matrix is constructed by combining the LogFbank features and the perceptual linear prediction coefficients, and a noise classifier based on the support vector machine is designed to classify the experimental noise. Then, to address the problem of “music noise” in traditional speech denoising methods, the voice activity detection threshold is adaptively updated according to the judgment result of background noise types, and the optimal parameters for the improved minima controlled recursive averaging noise estimation algorithm are obtained to accurately estimate current noise and reduce it. Finally, the performance of the proposed method is evaluated using a serial of experiments on different situations, and the experimental results prove the effectiveness and feasibility of our proposed method in this study.

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Retinopathy Identification in OCT images with A Semi-supervised Learning Approach via Complementary Expert Pooling and Expert-wise Batch Normalization

Retinopathy Identification in OCT Images with A Semi-supervi...

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Optoelectronics Global Conference (OGC)

作者： Arman Mohammad Nakib Yuan Li Yuemei Luo Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

In the medical field, the automatic detection of retinopathy using deep learning is an important application. However, traditional deep learning techniques face two significant challenges: the need for large amounts of labeled data, which are costly and difficult to obtain, and the imbalance of OCT ocular disease categories in practical applications, which can significantly affect model performance. This paper proposes a semi-supervised deep learning method based on complementary expert pooling (CEP). This method utilizes transfer learning with pre-trained models and enhances the model’s generalization capabilities through data augmentation strategies. Our approach implements a custom classification model on the VGG-16 architecture, employing CEP to model different category distributions across multiple experts and introducing Expert-wise Batch Normalization (EBN) to resolve feature distribution mismatches, thereby improving the model’s accuracy in recognizing minority classes. We evaluated our method on two datasets, using only 20% of the images as labeled data. Our method achieved $\mathbf{9 6. 5 5 \%}$ and $\mathbf{9 7. 7 8 \%}$ classification accuracies on these public datasets. Moreover, our approach demonstrated superior performance compared to various supervised learning methods using all available labeled data and other semi-supervised learning methods, proving its effectiveness in OCT ocular disease detection and its potential to assist clinicians in diagnosing ophthalmic diseases.

关键词： Accuracy image resolution Retinopathy Transfer learning Supervised learning Semisupervised learning Data models Batch normalization medical diagnostic imaging Diseases

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide image Classification 38

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

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Loading uniform Ag_(3)PO_(4)nanoparticles on three-dimensional peony-like WO_(3)for good stability and excellent selectivity towards NH_(3)at room temperature

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Chinese Physics B 2023年第8期32卷 251-259页

作者：邵星炎贾福超刘婷婷刘健诚王小梅尹广超吕娜周通 Ramachandran Rajan 刘波 Laboratory of Functional Molecules and Materials School of Physics and Optoelectronic EngineeringShandong University of TechnologyZibo 255000China School of Material Science and Engineering Shandong University of TechnologyZibo 255000China Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Translational Medical Center Zibo Central HospitalZibo 255036China

A heterojunction structure design is a very good method for improving the properties of semiconductors in many research *** method is employed in the present study to promote the gas-sensing performance of Ag_(3)PO_(4)nanocomposites at room temperature(25℃).A nanocomposite of Ag_(3)PO_(4)nanoparticles and three-dimensional peony-like WO_(3)(WO_(3)/Ag_(3)PO_(4))was successfully prepared by the precipitation *** crystalline phases were analyzed by xray diffraction and the microstructure was characterized by scanning electron microscopy and transmission electron *** chemical bonding states were analyzed by x-ray photoelectron *** gas-sensing performance of WO_(3)/Ag_(3)PO_(4)sensors was systematically explored at room *** composite sensors possessed a higher response and lower detection limit(1 ppm)to NH_(3)than those made of a single type of material;this is ascribed to the synergistic effect achieved by the heterojunction *** the different composite sensors tested,gas sensor A5W5(Ag_(3)PO_(4):WO3mass ratio of 5:5)displayed the highest response to NH_(3)at room ***,the A5W5 gas sensor exhibited relatively good stability and excellent selectivity to NH_(3).The A5W5 sensor also displayed a relatively good response under high *** gas-sensing mechanism of the WO_(3)/Ag_(3)PO_(4)sensors is explained in *** together,the as-prepared sensor is highly efficient at detecting NH_(3)and could be suitable for practical *** addition,this study also provides a new method for developing Ag_(3)PO_(4)-based sensors in the gas-sensing field.

关键词： Ag_(3)PO_(4) peony-like WO_(3) NH_(3) room temperature

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Tumor State-Space Network for High and Low Grade Glioma Classification 17

Tumor State-Space Network for High and Low Grade Glioma Clas...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Chen, Qijian Wang, Lihui Deng, Zeyu Wang, Li Ye, Chen Zhu, YueMin Guizhou University Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China University Lyon Insa Lyon Cnrs Inserm Irp Metislab Creatis UMR5220 U1206 Lyon69621 France

ISBN: (纸本)9798350387384

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment. © 2024 IEEE.

关键词： image enhancement

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OH-DRL: An AoI-Guaranteed Energy-Efficient Approach for UAV-Assisted IoT Data Collection

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5009-5022页

作者： Yang, Bowen Yu, Yao Hao, Xin Yeoh, Phee Lep Zhang, Junxiong Guo, Lei Li, Yonghui Northeastern University School of Computer Science and Engineering Ministry of Education Northeastern University and Key Laboratory of Intelligent Computing in Medical Image Shenyang110819 China Deakin University School of Information Technology VIC3216 Australia University of the Sunshine Coast School of Science Technology and Engineering Australia Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for unmanned aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data∗

A Hybrid Feature Selection Method Based on Binary Differenti...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xie, Weidong Li, Wei Fang, Yushan Chi, Yuhuan Yu, Kun Northeastern University Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University Medicine and Bioinformation Engineering Shenyang China

ISBN: (纸本)9781665468190

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the 'curse of dimensionality' and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm's variation process to balance the algorithm's search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated. © 2022 IEEE.

关键词： Feature Selection

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