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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shoujia Zhang Wei Li Weidong Xie Linjie Wang College of Computer Science and Engineering Northeastern University Shenyang Chain Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang Chain

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In recent decades, the rapid development of gene sequencing and computer technology has increased the growth of high-dimensional microarray data. Some machine learning methods have been successfully applied to it to help classify cancer. In most cases, high dimensionality and the small sample size of microarray data restricted the performance of cancer classification. This problem usually issolved bysome feature selection methods. However, most of them neglect the exploitation of relations among genes. This paper proposes a novel feature selection method by fusing multiple gene relation network information based on community detection (MGRCD). The proposed method divides all genes into different communities. Then, the genes most associated with cancer classification are selected from each community. The proposed method satisfies both maximum relevances gene with cancer and minimum redundancy among genes for the selected optimal feature subset. The experiment results show that the proposed gene selection method can effectively improve classification performance.

关键词： Sequential analysis Redundancy Machine learning Feature extraction Graph neural networks Data models Task analysis

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TransCoop: Cooperation of Transformers and CNNs for Camouflaged Object Segmentation

TransCoop: Cooperation of Transformers and CNNs for Camoufla...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wu, Fucai Li, Xuanya Zhang, Yuan Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Chenzhou423000 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Camouflaged object segmentation (COS) is a challenging task due to the existence of high intrinsic similarities between the object and background. To overcome this challenge, we pro-pose a new framework, called TransCoop, for COS through the cooperation of Transformers and convolutional neural net-works (CNNs). Specifically, Transformer is used to model global context and structural information for accurately positioning potential target objects. Meanwhile, a well-designed texture feature fusion module (TFFM) is used to fuse the features encoded by CNN with the shallow Transformer to fully mine the low-level features in the scene. Furthermore, we propose a noise removal module (NRM), which can eliminate the background noise of low-level features with the guidance of precise target location. Notably, our TFFM and NRM can effectively realize the interaction between Transformer and CNN features. Extensive experiments on four benchmark datasets demonstrate the superiority of our TransCoop against existing state-of-the-art methods. © 2022 IEEE.

关键词： Convolutional neural networks

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Pseudo Multi-Source Domain Extension and Selective Pseudo-Labeling for Unsupervised Domain Adaptive medical image Segmentation

Pseudo Multi-Source Domain Extension and Selective Pseudo-La...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaokang Liu Zhiqiang Wang Kai Hu Xieping Gao Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China College of Medical Imaging Laboratory and Rehabilitation Xiangnan University Chenzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China

Unsupervised domain adaptation (UDA) attracts extra attention in medical image processing because no additional labels are required when adapting to different distributions. In this work, we propose a novel unsupervised domain adaptation framework named as Domain Expansion and PseudoLabeling (DEPL). We extend the domain of a labeled source domain data to four different distributed domains and use adversarial learning to align the image appearance level and feature level from the four different domains to the unlabeled target domain. In addition, we propose a selective pseudolabeling mechanism, namely using strong confidence pseudolabeling to boost model performance. We evaluate our model for the MR to CT adaptation segmentation task on the public dataset MMWHS. Compared to seven other state-of-the-art segmentation methods, our DEPL achieves the best Dice similarity coefficient by 82.4%, which is at least 3.9% higher than the other UDA segmentation methods.

关键词： image segmentation Adaptation models Computed tomography Distributed databases Signal processing Adversarial machine learning Biomedical image processing

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Source-Free Unsupervised Domain Adaptation Fundus image Segmentation via Entropy Optimization and Anatomical Priors

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Procedia Computer Science 2024年 250卷 182-187页

作者： Yijia Chen Jiapeng Li Haoze Yu Lin Qi Yongchun Li College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China Shenyang Contain Electronic Technology Co. Ltd. Shenyang 110167 China

This research focuses on fundus image segmentation within a source-free domain adaptation framework, where the availability of source images during the adaptation phase is limited due to privacy concerns. Although Source-Free Unsupervised Domain Adaptation (SFUDA) methods have seen significant innovative developments in recent years, they still face several challenges which include suboptimal performance due to substantial domain discrepancies, reliance on potentially noisy or inaccurate pseudo-labels during the adaptation process, and a lack of integration with domain-specific prior knowledge. To address these issues, this paper proposes a SFUDA framework via Entropy Optimization and Anatomical Priors (EOAPNet). To alleviate the influence of the divergence between the source and target domains, EOAPNet primarily evaluates the uncertainty (i.e., entropy) of predictions on target domain data and improves the model by focusing on high-entropy pixels or regions. Additionally, a weak-strong augmentation mean-teacher scheme is introduced in EOAPNet, which can enhance the accuracy of pseudo-labels and reduce error propagation. Thirdly, by integrating an anatomical knowledge-based class ratio prior into the overall loss function in the form of a Kullback–Leibler (KL) divergence, EOAPNet also incorporates expert domain knowledge. EOAPNet yields comparable results to several state-of-the-art adaptation techniques in experiments on two retinal image segmentation datasets involving the RIM-ONE-r3 and Drishti-GS datasets.

关键词： Source-Free unsupervised domain adaptation Entropy Anatomical priors Mean teacher

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuaizheng Chen Chaolu Feng Wei Li Jinzhu Yang Dazhe Zhao School of Computer Science And Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction.

关键词： Training Measurement Deep learning image resolution Computational modeling Supervised learning Estimation image restoration Computational efficiency Convolutional neural networks

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Virtually-Federated Scheduling of Parallel Real-Time Tasks 42

Virtually-Federated Scheduling of Parallel Real-Time Tasks

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Jiang, Xu Guan, Nan Liang, Haochun Tang, Yue Qiao, Lei Yi, Wang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China City University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Uppsala University Sweden

ISBN: (纸本)9781665428026

Federated scheduling is a promising approach to schedule parallel real-time tasks, where each task exclusively executes on a set of dedicated processors. However, federated scheduling suffers significant resource wasting since a task typically only uses part of the processing capacity allocated to it, while the unused part cannot be shared with other tasks. To solve this problem, we present a virtually-federated scheduling approach, which both enjoys the good analyzability of federated scheduling and allows tasks to efficiently share processors with others. The main idea is to construct virtual processors on physical processors, and let a task exclusively execute on a set of virtual processors. As a physical processor is shared by virtual processors, tasks effectively share processors with each other. On the other hand, as each task exclusively executes on its own virtual processor set, the good analyzability of federated scheduling can be carried into to our virtually-federated scheduling approach. We conduct comprehensive performance evaluation to compare our proposed approach with existing methods of different types. Experiment results show that our approach consistently outperforms existing methods to a considerable extent under a wide range of parameter settings. ©2021 IEEE

关键词： Scheduling

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Glo-net: A dual task branch based neural network for multi-class glomeruli segmentation

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Computers in Biology and Medicine 2025年 186卷 109670-109670页

作者： Wang, Xiangxue Zhang, Jingkai Xu, Yuemei Huang, Yang Ming, Wenlong Jiao, Yiping Liu, Bicheng Fan, Xiangshan Xu, Jun Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing210044 China Department of Pathology Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing210008 China Institute of Nephrology Zhong Da Hospital Southeast University School of Medicine 210009 China

Accurate segmentation and classification of glomeruli are fundamental to histopathology slide analysis in renal pathology, which helps to characterize individual kidney disease. Accurate segmentation of glomeruli of different types faces two main challenges compared to traditional primitives segmentation in computational image analysis. Limited by small kernel size, traditional convolutional neural networks could hardly understand the complete context information of different glomeruli. Moreover, typical semantic segmentation networks lack adequate attention to difficult glomerular samples during the training process due to serious class imbalance between different glomeruli types. We propose a new deep learning approach, Glo-Net, which accurately segments and classifies glomeruli based on digitized pathology slides. Specifically, Glo-Net divides the traditional semantic segmentation network into two branches, i.e., segmentation and classification. While the segmentation branch specifically aims at localizing and delineating the boundary of individual glomerulus, the classification branch could focus on differentiating the glomerular types based on segmented pixels. In addition, an innovative loss function is added to the classification task to compensate for the class imbalance and minor types of glomeruli. The proposed network's average accuracy and F-score in classification tasks on the multi-institution datasets (including an external validation set) are 0.858 and 0.704, respectively. The average intersection over union (IoU) in segmentation tasks is 0.866. The Glo-Net demonstrates a 5 % improvement in classification accuracy, with up to 14 % increases for minor classes and an average 6 % IoU increase for segmentation tasks. Quantitative results show that our network achieves overall higher accuracy for segmentation and classification among nine subtypes of glomeruli compared to previous work with improved robustness and generalizability. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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MMBDE: A Two-stage Hybrid Feature Selection Method from Microarray Data

MMBDE: A Two-stage Hybrid Feature Selection Method from Micr...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

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

ISBN: (纸本)9781665401265

The discovery of diagnostically significant genes from microarray data is essential for disease diagnosis and drug research. However, the difficulty of analyzing microarray data comes from its high dimensionality and small sample size. Feature selection can effectively remove irrelevant and redundant features, reduce data dimensionality, and improve the accuracy of classifiers. This paper proposes a two-stage hybrid feature selection method MMBDE based on the improved min-Redundancy and Max-Relevance (mRMR) and the improved Binary Differential Evolution (BDE) algorithm. The improved mRMR is used to reduce the feature dimensionality at a coarse-scale significantly. In contrast, the improved BDE is used to refine the feature dimensionality at fine-scale further and select the best features. The experimental results show that MMBDE successfully reduces the dimensionality of microarray gene expression data, obtains high classification accuracy, and extracts effective features closely related to diseases from microarray gene expression data. The relevant datasets and codes can be obtained from https://***/xwdshiwo/MMBDE. © 2021 IEEE.

关键词： Feature Selection

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Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph

arXiv

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arXiv 2023年

作者： Yu, Song Gong, Shufeng Zhang, Yanfeng Yu, Wenyuan Yin, Qiang Tian, Chao Tao, Qian Yan, Yongze Yu, Ge Zhou, Jingren Northeastern University United States Alibaba Group China Shanghai Jiao Tong University China Chinese Academy of Sciences China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph which is much smaller than the original graph, and the lower layer has some disjoint subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, which significantly reduces the computation overhead and improves the performance of incremental graph processing. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08× on average (up to 36.66×) in response time. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xinhan Di Xiaokun Dai Xinkang Zhang Xinrong Chen Deepearthgo Academy for Engineering&Technology Fudan Universiry Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users’ virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results.

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