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Semi-Supervised Medical Image Segmentation Based on Frequency Domain Aware Stable Consistency Regularization

Journal of imaging informatics in medicine 2025年 2025 Jan 22页

作者： Yihao Ouyang Peipei Li Haixiang Zhang Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei 230009 Anhui China. School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 Anhui China. School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 Anhui China. peipeili@***. Center for Big Data and Population Health of IHM Anhui Medical University Hefei Anhui China. peipeili@***. Center for Big Data and Population Health of IHM Anhui Medical University Hefei Anhui China. Computer Centre The Second People's Hospital of Hefei Hefei 230011 Anhui China. Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei 230009 Anhui China.

With the advancement of deep learning models nowadays, they have successfully applied in the semi-supervised medical image segmentation where there are few annotated medical images and a large number of unlabeled ones. A representative approach in this regard is the semi-supervised method based on consistency regularization, which improves model training by imposing consistency constraints (perturbations) on unlabeled data. However, the perturbations in this kind of methods are often artificially designed, which may introduce biases unfavorable to the model learning in the handling of medical image segmentation. On the other hand, the majority of such methods often overlook the supervision in the Encoder stage of training and primarily focus on the outcomes in the later stages, potentially leading to chaotic learning in the initial phase and subsequently impacting the learning process of the model in the later stages. At the meanwhile, they miss the intrinsic spatial-frequency information of the images. Therefore, in this study, we propose a new semi-supervised medical image segmentation approach based on frequency domain aware stable consistency regularization. Specifically, to avoid the bias introduced by artificially setting perturbations, we first utilize the inherent frequency domain information of images, including both high and low frequencies, as the consistency constraint. Secondly, we incorporate supervision in the Encoder stage of model training to ensure that the model does not fail to learn due to the disruption of the original feature space caused by strong augmentation. Finally, extensive experimentation validates the effectiveness of our semi-supervised approach.

关键词： Consistency regularization Frequency domain Medical image segmentation Semi-supervision

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Improving Multi-Label Contrastive Learning by Leveraging Label Distribution

arXiv

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arXiv 2025年

作者： Chen, Ning Lyu, Shen-Huan Wu, Tian-Shuang Wang, Yanyan Tang, Bin Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies selected positive and negative samples based on the overlap between labels and used them for label-wise loss balancing. However, these methods suffer from a complex selection process and fail to account for the varying importance of different labels. To address these problems, we propose a novel method that improves multi-label contrastive learning through label distribution. Specifically, when selecting positive and negative samples, we only need to consider whether there is an intersection between labels. To model the relationships between labels, we introduce two methods to recover label distributions from logical labels, based on Radial Basis Function (RBF) and contrastive loss, respectively. We evaluate our method on nine widely used multi-label datasets, including image and vector datasets. The results demonstrate that our method outperforms state-of-the-art methods in six evaluation metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Higher-order Community Detection by Motif-based Modularity Optimization

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IEEE Transactions on big data 2025年

作者： Xiao, Jing Zou, Yu-Cheng Xu, Xiao-Ke Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Dalian Minzu University College of Information and Communication Engineering SEAC Key Laboratory of Big Data Applied Technology Dalian116600 China Beijing Normal University Computational Communication Research Center School of Journalism and Communication Beijing100875 China

Recently higher-order community detection based on network motifs has received increasing attention, because motif-based communities reflect not only mesoscale structures but also functional characteristics of real-life networks. In this study, we propose a Modularity Optimization method for Motif-based Community Detection (MOMCD). In order to approximate the global optimum in modularity optimization, an improved nature-inspired metaheuristic algorithm is proposed as optimization strategy. In addition, by comprehensively utilizing motif-based (higher-order) and edge-based (lower-order) structural information, a neighbor community modification operation and a local search operation are also designed to improve the quality of individuals and promote the convergence of MOMCD. Experimental results show that MOMCD is promising and competitive in identifying motif-based communities from synthetic and real-life networks, which outperforms state-of-the-art approaches in terms of quality and accuracy, and deepens our understanding of network structural and functional characteristics. © 2025 IEEE. All rights reserved,

关键词： Heuristic algorithms

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NIR-II fluorescence molecular tomography based on transformer encoder architecture

NIR-II fluorescence molecular tomography based on transforme...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Bao, Jie Xiao, Anqi Han, keyi Pei, Ziyu Fu, Lidan Tian, Jie Hu, Zhenhua CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9781510685987

Fluorescence molecular tomography (FMT) is a highly sensitive method for depicting the three-dimensional distribution of fluorescent targets within biological tissues. However, traditional FMT reconstruction can be affected by the severe photon scattering and the modeling errors of the photon propagation model, making it difficult to obtain accurate reconstruction results. Deep learning methods can directly learn the nonlinear mapping between the photon intensity on the surface and the internal light sources, providing a promising approach for addressing the inverse problem in FMT. Since the second near-infrared window (NIR-II, 1000-1700) fluorescence imaging has a higher tissue penetration depth and imaging contrast, this work constructs a deep learning model suitable for the fluorescence images in this spectrum. The model introduced in this study, akin to grasping the contextual nuances of word vectors in the domain of machine translation, endeavors to comprehend the process of photon transmission within biological tissues. Inspired by the transformer encoder, we proposed a network mainly consist of positional encoding, dual-head attention mechanism, residual connection, and fully connected layers. It has achieved significantly better results than comparative methods on the NIR-II dataset constructed by the Monte Carlo method. © 2025 SPIE.

关键词： Fluorescence imaging

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OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

arXiv

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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MA-Det: A Discriminative Morphology-Aware Detector for Cervical Lesion Cell Clumps

MA-Det: A Discriminative Morphology-Aware Detector for Cervi...

引用

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

Learned Video Compression With Refined Adaptive Flow Pyramid...

引用

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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GKG-LLM: A Unified Framework for Generalized knowledge Graph Construction

arXiv

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arXiv 2025年

作者： Zhang, Jian Wei, Bifan Qi, Shihao Zhu, Haiping Liu, Jun Lin, Qika School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China National University of Singapore Singapore

The construction of Generalized knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computing resources and usage perspectives. However, a key challenge in developing a unified framework for GKG is obstacles arising from task-specific differences. In this study, we propose a unified framework for constructing generalized knowledge graphs to address this challenge. First, we collect data from 15 sub-tasks in 29 datasets across the three types of graphs, categorizing them into in-sample, counter-task, and out-of-distribution (OOD) data. Then, we propose a three-stage curriculum learning fine-tuning framework, by iteratively injecting knowledge from the three types of graphs into the Large Language Models. Extensive experiments show that our proposed model improves the construction of all three graph types across in-domain, OOD and counter-task data. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

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Extracting High-order Connectivity of EEG-based Dynamic Functional Connectivity Networks for Diagnosis of Major Depressive Disorder 24

Extracting High-order Connectivity of EEG-based Dynamic Func...

引用

4th Asia-Pacific Artificial Intelligence and big data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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SR-FoT: A Syllogistic-Reasoning Framework of Thought for Large Language Models Tackling knowledge-based Reasoning Tasks

arXiv

引用

arXiv 2025年

作者： Wan, Wentao Yang, Zhuojie Chen, Yongcan Luo, Chenglin Wang, Ruilin Cai, Kehao Kang, Nan Lin, Liang Wang, Keze School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing China South China Normal University China

Deductive reasoning is a crucial logical capability that assists us in solving complex problems based on existing knowledge. Although augmented by Chain-of-Thought prompts, Large Language Models (LLMs) might not follow the correct reasoning paths. Enhancing the deductive reasoning abilities of LLMs, and leveraging their extensive built-in knowledge for various reasoning tasks, remains an open question. Attempting to mimic the human deductive reasoning paradigm, we propose a multi-stage Syllogistic-Reasoning Framework of Thought (SR-FoT) that enables LLMs to perform syllogistic deductive reasoning to handle complex knowledge-based reasoning tasks. Our SR-FoT begins by interpreting the question and then uses the interpretation and the original question to propose a suitable major premise. It proceeds by generating and answering minor premise questions in two stages to match the minor premises. Finally, it guides LLMs to use the previously generated major and minor premises to perform syllogistic deductive reasoning to derive the answer to the original question. Extensive and thorough experiments on knowledge-based reasoning tasks have demonstrated the effectiveness and advantages of our SR-FoT. Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge based systems

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