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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Jiao, Xu Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

The number of patients with nonalcoholic fatty liver is increasing. Liver fat quantification is the most reliable indicator for the diagnosis of non-alcoholic fatty liver disease, and it is of great value for the early diagnosis and condition monitoring of non-alcoholic fatty liver disease. Therefore, it is of great significance to quantitatively analyze liver fat. In this work, we utilize the superpixel segmentation method based on depth neural network to preprocess the MRI image, we can simplify the representation of the image and obtain good organ boundary information. In addition, we also understand the popular involution operator and improve it through combining the medical image segmentation model u-net to obtain a better region of interest. The segmentation accuracy of the proposed method is 1.82 percentage points higher than that of u-net model. On this basis, the proton density and fat fraction of liver are calculated by medical formula, achieving the sequencing measurement of liver fat. The experimental results show that the proposed method is efficient and feasible. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Robust Federated Semi-Supervised Learning for Medical Image Classification via Pseudo-Label Filtering

Robust Federated Semi-Supervised Learning for Medical Image ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ziwei Wang Shuyu Guo Mingzhu Zhu Tian Bai College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Federated learning (FL) enables collaborative model training across multiple medical institutions to ensure data security. However, due to the variations in medical imaging equipment and regions at different medical institutions, FL methods usually suffer from insufficient data annotations and irrelevant noise within private datasets. To address these issues, a robust federated semi-supervised learning method via pseudo-label filtering (PFRFed) is introduced to utilize unlabeled data while mitigating the impact of noise data. Compared with existing federated semi-supervised learning methods, we propose a pseudo-label filtering mechanism with double dynamic thresholds, which allows the model to adopt more unlabeled data by adjusting the confidence and entropy thresholds at each stage of model training. Moreover, to reduce the degradation caused by noise data in private datasets from different clients, a noise-tolerant loss function and a grouping aggregation method based on the local model similarity are employed. The comparative experiments demonstrate the effectiveness of PFRFed, which has achieved the best classification accuracy of 95.20% and 88.72% on two public medical datasets. Also, PFRFed exhibits heightened resilience to variations in noisy data ratio and labeled data ratio, reaffirming its versatility and robustness.

关键词： Training Filtering Biological system modeling Noise Semisupervised learning Data models Robustness Noise measurement Biomedical imaging Image classification

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Enhancing Nested Named Entity Recognition in Chinese EMRs: A Collaborative Approach

Enhancing Nested Named Entity Recognition in Chinese EMRs: A...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongtao Yue Xiaoyue Feng Hanchi Xu Haoxuan Chu Yushi Wang Renchu Guan Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Cardiovascular Center The First Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Named entity recognition (NER) in electronic medical records (EMRs) is critical for identifying medical entities, constructing medical knowledge graphs, and supporting clinical decision-making. However, the scarcity of EMR datasets and the challenges posed by the complexity of Chinese medical texts hinder progress in this area. To address these issues, we introduce CMR-NER, a nested named entity recognition model that enhances entity prediction by integrating multiple features and considering a global view of entity boundaries. Leveraging the robust generalization capabilities of the large language model, CMR-NER involves collaboration with medical experts to ensure accuracy and reduce annotation costs. Additionally, we present HF-NER, a high-quality dataset specifically curated for Chinese EMR entity recognition focusing on heart failure. This dataset, constructed through a methodology combining ChatGPT’s capabilities and expert validation, is a significant contribution to the field. It facilitates a comprehensive evaluation of CMR-NER. Experimental results demonstrate that our approach achieves comparable or superior performance to existing methods, underscoring its effectiveness in this challenging domain.

关键词： Costs Annotations Large language models Collaboration Named entity recognition Medical services knowledge graphs Predictive models Chatbots Cardiovascular diseases

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Research on Ethnic Pattern Generation Based on Generative Adversarial Networks

Research on Ethnic Pattern Generation Based on Generative Ad...

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International Workshop on Advanced computational Intelligence (IWACI)

作者： Hao Wu Wenze He Xiongfei Li Yanchun Liang Zhuhai Sub Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Zhuhai College of Science and Technology Zhuhai China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

In this paper, we take Mongolian ethnic patterns as the research object and conduct a technical study on ethnic pattern generation and super-resolution reconstruction based on deep learning generative adversarial networks to address the problems of low quality of ethnic pattern data collection and lack of innovation. In this paper, we use a database including 1621 Mongolian ethnic patterns, train StyleGAN2 on the pre-processed images, and generate images After that, we use ESRGAN to perform super-resolution reconstruction on the generated images to generate high-resolution patterns with Mongolian style. The model designed in this paper can generate high quality images with ethnic characteristics in the case of insufficient original data. Compared with the time-consuming and labor-intensive traditional ethnic pattern design methods, the model designed in this paper lowers the threshold of ethnic pattern innovation and contributes to the innovative design of ethnic patterns to a certain extent, which has some positive significance for the protection and inheritance of ethnic patterns.

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Information Entropy Invariance: Enhancing Length Extrapolation in Attention Mechanisms

arXiv

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arXiv 2025年

作者： Li, Kewei Kong, Yanwen Xu, Yiping Su, Jianlin Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Moonshot AI Ltd. Beijing100086 China

Since the emergence of research on improving the length extrapolation capabilities of large models in 2021, some studies have made modifications to the scaling factor in the scaled dot-product attention mechanism as part of their proposed methods without rigorous theoretical justifications. To fill this gap, we propose two new scaled temperatures based on entropy invariance to enhance length extrapolation. First, a training-free method InfoScale is designed for dot-product attention, and preserves focus on original tokens during length extrapolation by ensuring consistent entropy. Second, we theoretically analyze the impact of scaling (CosScale) on cosine attention. Experimental data demonstrates that combining InfoScale and CosScale achieves state-of-the-art performance on the GAU-α model with a context window extended to 64 times the training length, and outperforms seven existing methods. Our analysis reveals that significantly increasing CosScale approximates the Windowed Attention, and highlights the significance of attention score dilution as a key challenge in long-range context handling. The code and data are available at https://***/HT-NEKO/InfoScale. © 2025, CC BY-NC-ND.

关键词： Data handling

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Few-Shot Data Completion for New Tasks in Sparse Crowdsensing

Few-Shot Data Completion for New Tasks in Sparse Crowdsensin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Mijia Zhang Bo Yang Yang Xu Zixuan Song Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated Data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

关键词： Performance evaluation Accuracy Crowdsensing Filtering Training data Data models Real-time systems

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GraCEImpute: A novel graph clustering autoencoder approach for imputation of single-cell RNA-seq data

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Computers in Biology and Medicine 2025年 184卷 109400-109400页

作者： Wang, Yueying Li, Kewei Zhang, Ruochi Fan, Yusi Huang, Lan Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang550025 China

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://***/supp/. © 2024 Elsevier Ltd

关键词： Gene expression

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Mask2Edge: Masking dependencies and dynamically capturing pixel differences in edge detection

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Expert Systems with Applications 2025年 286卷

作者： Zhou, Jianhang Zhao, Hongwei Qu, Daikun Xing, Long College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun130012 China Army Artillery and Air Defense Academy Sergeant School Liaoning Shenyang110000 China

Edge detection plays an important role in computer vision tasks. Deep learning-based edge detectors commonly rely on encoding the long and short-term dependencies of pixel values to mine contextual information. They strongly focus on all positions in the image, ignoring the potential issue of over-encoding. Furthermore, most of these models have not attempted to leverage the inherent properties of edges. In this paper, we introduce a query-based edge detector named Mask2Edge, which is capable of masking dependencies and dynamically capturing pixel differences. Specifically, we first devise a masking strategy based on the sparsity of edges to alleviate the over-encoding issue. We propose a Region-guided Masked Attention, which adapts to edge detection and is capable of constraining cross-attention with appropriate masking intensity to extract relatively complete local features. Subsequently, we design a structure to capture the pixel differences that can help identify edges. We introduce dynamic convolutions into edge detection and refine the application scope and generation method of attention weights to effectively perceive changes in pixel gradients. Extensive experiments demonstrate the superiority of Mask2Edge compared with state-of-the-art methods. The source code is available at https://***/zhoujh2020/Mask2Edge. © 2025 Elsevier Ltd

关键词： Edge detection

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Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport 25

Enhancing Unsupervised Graph Few-shot Learning via Set Funct...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and Data Mining V.1

作者： Yonghao Liu Fausto Giunchiglia Ximing Li Lan Huang Xiaoyue Feng Renchu Guan Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (纸本)9798400712456

Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers. Recent advancements in graph few-shot learning models have exhibited superior performance across diverse applications. Despite their successes, several limitations still exist. First, existing models in the meta-training phase predominantly focus on instance-level features within tasks, neglecting crucial set-level features essential for distinguishing between different categories. Second, these models often utilize query sets directly on classifiers trained with support sets containing only a few labeled examples, overlooking potential distribution shifts between these sets and leading to suboptimal performance. Finally, previous models typically require necessitate abundant labeled data from base classes to extract transferable knowledge, which is typically infeasible in real-world scenarios. To address these issues, we propose a novel model named STAR, which leverages Set funcTions and optimAl tRansport for enhancing unsupervised graph few-shot learning. Specifically, STAR utilizes expressive set functions to obtain set-level features in an unsupervised manner and employs optimal transport principles to align the distributions of support and query sets, thereby mitigating distribution shift effects. Theoretical analysis demonstrates that STAR can capture more task-relevant information and enhance generalization capabilities. Empirically, extensive experiments across multiple datasets validate the effectiveness of STAR. Our code can be found https://***/KEAML-JLU/STAR here.

关键词： few-shot learning

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A Driving Area Detection Algorithm Based on Swin Transformer

A Driving Area Detection Algorithm Based on Swin Transformer

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Shuang Liu Ying Li College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China

The detection of drivable areas holds immense significance within the perception system of autonomous vehicles. This capability enables intelligent vehicles to gain a comprehensive understanding of the current road conditions and effectively identify safe areas suitable for safe navigation. Convolutional Neural Networks (CNNs) have achieved significant progress in the application of drivable areas detection, particularly in semantic segmentation. However, most existing CNN-based methods for drivable area detection only consider information within a local neighborhood, thus failing to effectively capture long-range dependencies. To tackle this concern, we propose a novel U-shaped network based on the Swin Transformer. The Swin Transformer block was employed as the fundamental element of both the encoder and decoder. Additionally, an upsampling layer was introduced in the decoder to effectively restore the resolution of the feature map. We evaluate our approach on the Cityscapes dataset, a publicly available benchmark for autonomous driving. Experimental results verify the exceptional performance of our proposed method, achieving an impressive mean Intersection over Union (mIoU) score of 91.61%. Our method outperforms existing state-of-the-art techniques in road segmentation detection, further demonstrating its superiority.

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