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

作者： Fu, Yanyu Quanyu, E. Zhou, Shangqi Ouyang, Xinyu Yang, Jinzhu Grzegorzek, Marcin 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

ISBN: (纸本)9789819708543

Intra-abdominal fat is a crucial factor in determining metabolic syndrome and insulin resistance. Therefore, high levels of intra-abdominal fat are always accompanied by a series of diseases such as diabetes, ischemia, hypertension, and dyslipidemia. Although methods such as the ultrasonic method and electrical impedance method have been widely used to calculate body fat tissue in recent years, these methods only provide qualitative analysis of the overall degree of obesity. However, for quantitative analysis of abdominal fat and adipose tissue distribution in the human body, the computed tomography (CT) fat segmentation method is necessary. This paper focuses on machine learning, image segmentation, and multi-feature fusion of mean clustering as its main technical directions. In terms of application, it aims to perform a quantitative analysis of the distribution and proportion of intra-abdominal fat in the abdominal cavity of the human body. The study concluded that the quantitative analysis of fat after the fusion method is more accurate, with a subcutaneous fat accuracy rate of 84.26% and a visceral fat accuracy rate of 70.20%. It can effectively, conveniently, and objectively analyze the degree of obesity in the human body. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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An Adaptive Speech Noise Reduction Method Based on Noise Classification 13

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

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Niu, Ben Wei, Yangjie College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

ISBN: (纸本)9798350315196

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. © 2023 IEEE.

关键词： Support vector machines

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Dual-Granularity Contrastive Learning for Session-Based Recommendation 9th

Dual-Granularity Contrastive Learning for Session-Based Rec...

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

作者： Wang, Zihan Wu, Gang Wang, Haotong School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9783031466601

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel dual-granularity CL framework. Specifically, two extra augmentation views with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data, with which we can explore connections of items on latent factor independently and generate factor-level embeddings. At item-level, the star graph is deployed as the augmentation method. By setting an additional satellite node, non-adjacent nodes can establish additional connections through satellite nodes instead of reducing the connections of the original graph, so data sparsity can be avoided. Compare the learned embeddings of these two views with the learned embeddings of the original view to achieve CL at two granularities. Finally, the item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Benchmarking

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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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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

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

ISBN: (纸本)9798350386226

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. © 2024 IEEE.

关键词： Self-supervised learning

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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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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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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An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers

An Abnormal Audio Generation Method for Fault Diagnosis of P...

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

作者： Ben Niu Yangjie Wei Ke Zhang Zhuoran Yu Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model training, resulting in limited diagnostic performance. To address this problem, we propose an abnormal audio generation method based on the improved cycle generative adversarial networks (ImCycleGAN) to augment the limited training dataset. In the ImCycleGAN, the generator and discriminator are redesigned and adversarially trained to generate the realistic-like audios of six abnormal statuses. Moreover, we combine adversarial, cycle consistency and identity mapping losses to optimize the training process of ImCycleGAN and enhance its ability to capture nonlinear features of audios. Finally, the generated data is evaluated in terms of similarity and fault classification. Experimental results show that our method can generate abnormal audios with high similarity to the real ones, and significantly improve the classification accuracy of existing fault diagnosis models.

关键词： Fault diagnosis Training Support vector machines Accuracy Signal processing Generative adversarial networks Generators Power transformers Speech processing Long short term memory

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A learnable self-supervised task for unsupervised domain adaptation on point cloud classification and segmentation

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Frontiers of Computer Science 2023年第6期17卷 147-149页

作者： Shaolei LIU Xiaoyuan LUO Kexue FU Manning WANG Zhijian SONG Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai 200032China Digital Medical Research Center School of Basic Medical ScienceFudan UniversityShanghai 200032China

1 Introduction Deep neural networks have exhibited excellent performance in supervised tasks on point clouds,such as classification,segmentation[1]and registration[2].In supervised learning schemes,manual labeling of massive point clouds is needed for model ***,point clouds captured from different scenarios exist inevitable distribution discrepancy,and model trained from one domain always generalize badly in another *** reduce the doamin distribution discrepancy,many studies[3–6]have emerged for point cloud unsupervised domain adaptation(UDA)by learning domain-invariant features,where[5]proposed using adaptive nodes to align the local features between the source and the target domains[3,4],and[6]proposed utilizing self-supervised tasks to help capture highly transferable feature representations.

关键词： point cloud utilizing

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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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