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arXiv 2023年

作者： Wang, Huadeng Xu, Hao Li, Bingbing Pan, Xipeng Zeng, Lingqi Lan, Rushi Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Department of Pathology Ganzhou Municipal Hospital Ganzhou341000 China

Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-consuming. In this paper, we propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named Mdet is proposed to detect as many mitoses as possible. In the second stage, a classification network Mclass is proposed to refine the results of the first stage. In addition, the attention mechanism, normalization method, and hybrid anchor branch classification subnet are introduced to improve the overall detection performance. Our method achieves the current highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated our method on the GZMH dataset released by our research team for the first time and reached the highest F1-score of 0.563, which is also better than multiple classic detection networks widely used at present. It confirmed the effectiveness and generalization of our method. The code will be available at: https://***/antifen/mitosis-nuclei-detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Grading

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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation

A Lightweight Chinese Summarization Generation Method Based ...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Shiyue Liu Yannan Xiao Xueyong Wang Wenming Huang Zhenrong Deng Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer and Information Security Guilin University of Electronic Technology Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models.

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Sliding Mode Control for Quadrotor-Slung Load Transportation System with State Constraints*

Sliding Mode Control for Quadrotor-Slung Load Transportation...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Feng Ding Chong Sun Yong Ai Jian Huang School of Computer Science South-Central Minzu University Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and automation Huazhong University of Science and Technology Wuhan Hubei China

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.

关键词： Attitude control Trajectory tracking Simulation Force Dynamics Transportation Stability analysis

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Semi-Supervised Classification of Data Streams by BIRCH Ensemble and Local Structure Mapping

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Journal of computer Science & Technology 2020年第2期35卷 295-304页

作者： Yi-Min Wen Shuai Liu Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Many researchers have applied clustering to handle semi-supervised classification of data streams with concept ***,the generalization ability for each specific concept cannot be steadily improved,and the concept drift detection method without considering the local structural information of data cannot accurately detect concept *** paper proposes to solve these problems by BIRCH(Balanced Iterative Reducing and Clustering Using Hierarchies)ensemble and local structure *** local structure mapping strategy is utilized to compute local similarity around each sample and combined with semi-supervised Bayesian method to perform concept *** a recurrent concept is detected,a historical BIRCH ensemble classifier is selected to be incrementally updated;otherwise a new BIRCH ensemble classifier is constructed and added into the classifier *** extensive experiments on several synthetic and real datasets demonstrate the advantage of the proposed algorithm.

关键词： semi-supervised classification clustering data stream concept drift

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity 39

Topology-Aware 3D Gaussian Splatting: Leveraging Persistent ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shen, Tianqi Liu, Shaohua Feng, Jiaqi Ma, Ziye An, Ning Department of Computer Science City University of Hong Kong Hong Kong Image Processing Center Beihang University China Shen Yuan Honors College Beihang University China Research Institute of Mine Artificial Intelligence China Coal Research Institute China State Key Laboratory of Intelligent Coal Mining and Strata Control China

ISBN: (纸本)157735897X

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-level structural integrity due to incomplete initial geometric coverage, and inadequate feature-level integrity from insufficient topological constraints during optimization. To overcome these limitations, Topology-GS incorporates a novel interpolation strategy, Local Persistent Voronoi Interpolation (LPVI), and a topology-focused regularization term based on persistent barcodes, named PersLoss. LPVI utilizes persistent homology to guide adaptive interpolation, enhancing point coverage in low-curvature areas while preserving topological structure. PersLoss aligns the visual perceptual similarity of rendered images with ground truth by constraining distances between their topological features. Comprehensive experiments on three novel-view synthesis benchmarks demonstrate that Topology-GS outperforms existing methods in terms of PSNR, SSIM, and LPIPS metrics, while maintaining efficient memory usage. This study pioneers the integration of topology with 3D-GS, laying the groundwork for future research in this area. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gaussian distribution

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Algorithmic Research Overview on Graph Coloring Problems

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computer Engineering and Applications 2024年第18期60卷 66-77页

作者： Song, Jiahuan Wang, Xiaofeng Hu, Simin Jia, Jingwei Yan, Dong School of Computer Science and Engineering North Minzu University Yinchuan750021 China Laboratory of Image & Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

The graph coloring problem (GCP) is a classic combinatorial optimization problem that has been widely applied in various fields such as mathematics, computer science, and biological science. Due to the NP hard nature of graph coloring problems, there is currently no precise algorithm in polynomial time to solve the problem. In order to provide an efficient algorithm for solving this problem, it is necessary to review the existing algorithms. It mainly divided into intelligent optimization algorithms, heuristic algorithms, reinforcement learning algorithms, etc., comparative analysis is carried out from the aspects of algorithm principles, improvement ideas, performance and accuracy, summarizing the advantages and disadvantages of algorithms, and pointing out the research direction and algorithm design path of GCP, which has guiding significance for the research of related problems. © 2024 Journal of computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： Optimization algorithms

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Topology-Aware 3D Gaussian Splatting: Leveraging Persistent Homology for Optimized Structural Integrity

arXiv

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arXiv 2024年

关键词： Interpolation

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Learning Probabilistic Coordinate Fields for Robust Correspondences

arXiv

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arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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A2B: Anchor to Barycentric Coordinate for Robust Correspondence

arXiv

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arXiv 2023年

作者： Zhao, Weiyue Lu, Hao Cao, Zhiguo Li, Xin The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Lane Department of Computer Science and Electrical Engineering West Virginia University Commonwealth of Virginia Morgantown 15461 United States

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce DEGREE, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. DEGREE can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that DEGREE alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, DEGREE also reports competitive performance in the third image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences. Copyright © 2023, The Authors. All rights reserved.

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