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

作者： Ziyang Yin Qian Huang Yulin Chen Hao Lu College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automated detection of cervical lesion cell clumps is crucial for cervical cancer screening. However, the dense packing and overlap of cells, caused by adhesion molecules, make detection challenging. To address this issue, we propose the Morphology-Aware Detector (MA-Det). Specifically, by innovatively employing Deformable Convolution, we propose the Dynamic Context Aggregation Module (DCAM) which dynamically captures contextual information, improving the feature representation of lesion cells. To enhance discrimination in high-density regions, we design the Distance-Weighted Interaction Module (DWIM) and Adaptive Morphology-Aware (AMA) loss into the detection head. These components improve spatial awareness by utilizing interactions between cell features and proposals. In particular, our method achieves state-of-the-art performance on the CDetector and CRIC datasets while significantly reducing parameters.

关键词： Convolution Adhesives Detectors Medical services Feature extraction Complexity theory Lesions Proposals Speech processing Cervical cancer

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multiPI-TransBTS: A multi-path learning framework for brain tumor image segmentation based on multi-physical information

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Computers in Biology and Medicine 2025年 191卷 110148-110148页

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. © 2025

关键词： Decoding

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How do we improve the reproducibility and accuracy of EIS and Tafel testing in corrosion science?

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Surface Science and Technology 2025年第1期3卷 1-22页

作者： Shao, Zexi Ma, Mingyuan Liu, Xuhui Yuan, Sicheng Lin, Dan Bai, Ziheng Hu, Haodong Yu, Jingtai Du, Yafei Zhu, Yanji Wang, Huaiyuan School of Chemical Engineering and Technology and State Key Laboratory for Chemical Engineering Tianjin University Tianjin P.R. China Tianjin Key Laboratory of Chemical Process Safety and Equipment Technology Tianjin University Tianjin P.R. China School of Materials Science and Engineering Northeastern University Shenyang China School of Materials and New Energy Ningxia University Yinchuan P.R. China Institute for Advanced Materials and Technology National Materials Corrosion and Protection Scientific Data Center University of Science and Technology Beijing Beijing China

Electrochemical is crucial in corrosion, energy storage, and electrocatalysts and are essential for mitigating carbon emissions. However, deviations in electrochemical testing can result from experiment errors and electrode preparation differences. Therefore, electrochemical experiment results are required to assess the reproducibility. Herein we found that the surface resistance is a key factor of electrochemical testing, and the difference of surface resistance could inform future anode oxidation devices, and enhance the reproducibility and accuracy of electrochemical testing.

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NucleiFormer: A Nuclei Segmentation Model Optimized by Joint Haar Wavelet and Adaptive Feature Calibration

NucleiFormer: A Nuclei Segmentation Model Optimized by Joint...

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

作者： Yulin Chen Qian Huang Zhijian Wang Ziyang Yin Meng Geng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Nucleus segmentation plays a vital role in medical image analysis. However, existing segmentation methods frequently encounter hurdles, such as the loss of crucial image details during downsampling and issues like noise and spatial displacement. In this study, we propose NucleiFormer, where Haar wavelet transforms are employed in the encoder to replace conventional downsampling techniques. Additionally, we utilize an adaptive feature calibration module to align and calibrate features of different scales, reduce feature redundancy, suppress noise, and enhance the spatial awareness of the model. Extensive experiments validate our model’s superior segmentation performance in accurately delineating nucleus boundaries and minimizing errors.

关键词： Wavelet transforms Adaptation models Image segmentation Image analysis Noise Termination of employment Calibration Speech processing Optimization Biomedical imaging

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Learned Video Compression With Refined Adaptive Flow Pyramid And Coordinate-Aware Attention

Learned Video Compression With Refined Adaptive Flow Pyramid...

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

作者： Qian Huang Wenting Liu Xin Li Yiming Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression efficiency, they still have limitations. SpyNet often loses details and fails to fully utilize the feature extraction capabilities of deep networks. Furthermore, bilinear interpolation inherently attenuates high-frequency information, leading to frame blurring and distortion. In this paper, we propose a novel video compression algorithm. To overcome the limitations of SpyNet, we propose a refined adaptive flow pyramid network. This network uses a multi-scale feature pyramid to capture more details. Moreover, we use an iterative cost volume refinement engine that improves the feature representation of the network and iteratively improve the accuracy of motion estimation. In addition, to overcome the limitation of bilinear interpolation, we propose a coordinate-aware attention module, which captures more high-frequency information to improve the accuracy of motion compensation. Experimental results show that our method outperforms VTM-13.2 (LDP) in terms of PSNR.

关键词： Interpolation Adaptive systems Costs Acoustic distortion Accuracy Motion estimation Video compression Feature extraction Motion compensation Speech processing

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Partial Label Learning via Conditional-Label-Aware Disambiguation

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Journal of Computer Science & Technology 2021年第3期36卷 590-605页

作者： Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry of Education Beijing 100087China School of Information Renmin University of ChinaBeijing 100087China

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth *** paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing *** existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise *** experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

关键词： disambiguation partial label learning similarity and dissimilarity weak supervision

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Dual Encoders for Diffusion-based Image Inpainting

Dual Encoders for Diffusion-based Image Inpainting

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

作者： Dezhi Zheng Kaijun Deng Jinbao Wang Linlin Shen Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current diffusion-based inpainting models struggle to preserve unmasked regions or generate highly coherent content. Additionally, it is hard for them to generate meaningful content for 3D inpainting. To tackle these challenges, we design a plug-and-play branch that runs through the entire generation process to enhance existing models. Specifically, we utilize dual encoders - a Convolutional Neural Network (CNN) encoder and the pre-trained Variational AutoEncoder (VAE) encoder, to encode masked images. The latent code and the feature map from the dual encoders are fed to diffusion models simultaneously. In addition, we apply Zero-padded initialization to solve the problem of mode collapse caused by this branch. Experiments on BrushBench and EditBench demonstrate that models with our plug-and-play branch can improve the coherence of inpainting, and our model achieves new state-of-the-art results.

关键词： Visualization Three-dimensional displays Codes Autoencoders Coherence Signal processing Diffusion models Decoding Convolutional neural networks Speech processing

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Improving Parameter Estimation and Defensive Ability of Latent Dirichlet Allocation Model Training Under Rényi Differential Privacy

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Journal of Computer Science & Technology 2022年第6期37卷 1382-1397页

作者： Tao Huang Su-Yun Zhao Hong Chen Yi-Xuan Liu Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry of Education Beijing 100087China School of Information Renmin University of ChinaBeijing 100087China

Latent Dirichlet allocation(LDA)is a topic model widely used for discovering hidden semantics in massive text *** Gibbs sampling(CGS),as a widely-used algorithm for learning the parameters of LDA,has the risk of privacy ***,word count statistics and updates of latent topics in CGS,which are essential for parameter estimation,could be employed by adversaries to conduct effective membership inference attacks(MIAs).Till now,there are two kinds of methods exploited in CGS to defend against MIAs:adding noise to word count statistics and utilizing inherent *** two kinds of methods have their respective *** sampled from the Laplacian distribution sometimes produces negative word count statistics,which render terrible parameter estimation in *** inherent privacy could only provide weak guaranteed privacy when defending against *** is promising to propose an effective framework to obtain accurate parameter estimations with guaranteed differential *** key issue of obtaining accurate parameter estimations when introducing differential privacy in CGS is making good use of the privacy budget such that a precise noise scale is *** is the first time that R′enyi differential privacy(RDP)has been introduced into CGS and we propose RDP-LDA,an effective framework for analyzing the privacy loss of any differentially private ***-LDA could be used to derive a tighter upper bound of privacy loss than the overestimated results of existing differentially private CGS obtained byε-*** RDP-LDA,we propose a novel truncated-Gaussian mechanism that keeps word count statistics *** we propose distribution perturbation which could provide more rigorous guaranteed privacy than utilizing inherent *** validate that our proposed methods produce more accurate parameter estimation under the JS-divergence metric and obtain lower precision and recall when defending against MIAs.

关键词： latent Dirichlet allocation parameter estimation membership inference attack Rényi differential privacy

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Numerical simulation of human hydrodynamic characteristics in uniform current impacts

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Ocean engineering 2025年 333卷

作者： Dong, Guo-Hai Liu, Zhi-Qiang Zheng, Zhen-Jun Ma, Xiao-Zhou State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian116024 China School of Ocean Science and Engineering Hainan University 58 Renmin Avenue Hainan Haikou570228 China

In the present study, an OpenFOAM solver based on the Reynolds-averaged Navier-Stokes equations is used to simulate the uniform current interaction with a natural-standing human body. We analyze the differences in the flow field, pressure field, and force characteristics of Chinese adult males of representative height and weight when standing naturally with their faces or backs to the incoming flow, investigate the effects of different water depths and flow velocities on the force characteristics of the human body, as well as conduct a dimensionless analysis of the force characteristics of the human body through regression modeling. The study also considered the effects of different standing orientations on human force and stability assessment. Results show that the overturning moment on the human body is mainly affected by the standing orientation, and the overturning moment on the human body is smaller when standing backward to the inflow compared to facing the inflow. The force on the human body in the streamwise direction shows a strong linear correlation with the Froude number, the transverse force is more affected by the human body's shape, and the vertical force is mainly affected by the depth of the water. Comparing the different standing orientations, the human body is less likely to sliding instability when facing the oncoming current sideways, and less likely to toppling instability when facing the incoming current. © 2025 Elsevier Ltd

关键词： Regression analysis

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Efficient Model Store and Reuse in an OLML database System

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Journal of Computer Science & Technology 2021年第4期36卷 792-805页

作者： Jian-Wei Cui Wei Lu Xin Zhao Xiao-Yong Du Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China

Deep learning has shown significant improvements on various machine learning tasks by introducing a wide spectrum of neural network ***,for these neural network models,it is necessary to label a tremendous amount of training data,which is prohibitively expensive in *** this paper,we propose OnLine Machine Learning(OLML)database which stores trained models and reuses these models in a new training task to achieve a better training effect with a small amount of training *** efficient model reuse algorithm AdaReuse is developed in the OLML ***,AdaReuse firstly estimates the reuse potential of trained models from domain relatedness and model quality,through which a group of trained models with high reuse potential for the training task could be selected ***,multi selected models will be trained iteratively to encourage diverse models,with which a better training effect could be achieved by *** evaluate AdaReuse on two types of natural language processing(NLP)tasks,and the results show AdaReuse could improve the training effect significantly compared with models training from scratch when the training data is *** on AdaReuse,we implement an OLML database prototype system which could accept a training task as an SQL-like query and automatically generate a training plan by selecting and reusing trained *** studies are conducted to illustrate the OLML database could properly store the trained models,and reuse the trained models efficiently in new training tasks.

关键词： model selection model reuse OnLine Machine Learning(OLML)database

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