PATTERN RECOGNITION模式识别
PATTERN RECOGNITION(英文缩写 PATTERN RECOGN),ISSN 0031-3203,eISSN 1873-5142,中文译名:模式识别 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 8.518 | Q1 |
| 2022 | 8.000 | Q1 |
| 2023 | 7.500 | Q1 |
| 2024 | 7.600 | Q1 |
| 2025 | 9.100 | Q1 |
PATTERN RECOGNITION 最新收录文献
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1. Hierarchical spatial perception network and SAM-assisted uncertainty suppression for medical image segmentation.
1. 用于医学图像分割的层次化空间感知网络与SAM辅助的不确定性抑制PMID:日期:2026-06-17[中文摘要] 准确的医学图像分割对于图像引导的临床操作至关重要，然而解剖结构的复杂性和变异性使其极具挑战性。为解决这一问题，我们提出了一种新颖的分割框架，该框架集成了分层空间感知网络（HSP-Net）和基础模型（SAM：Segment Anything Model）辅助的不确定性抑制（SUS）策略。HSP-Net通过组金字塔注意力（GPA）模块增强了经典U-Net架构，该模块通过通道池化注意力（CPA）块和空间分层注意力（SHA）块动态强调多尺度上的判别性特征。SUS策略利用SAM识别难以分割的像素并降低预测不确定性。通过将语义特征学习与基础模型辅助的细化相结合，我们的框架在五个公开数据集（即BUSI、CVC、ISIC2017、ISIC2018和Synapse）上取得了有前景的性能。大量实验表明，该框架始终优于U-Net及其多种变体（如UNeXt和UTNet），平均Dice得分为0.8815，95%豪斯多夫距离为7.3867，同时在多模态医学图像上展现出稳健的泛化能力。源代码已公开于https://github.com/wmuLei/hsp-net。
[英文摘要] Accurate medical image segmentation is critical for image-guided clinical procedures, yet the complexity and variability of anatomical structures make it highly challenging. To address this, we propose a novel segmentation framework that integrates a Hierarchical Spatial Perception network (HSP-Net) and a foundation model (SAM: Segment Anything Model)-assisted uncertainty suppression (SUS) strategy. HSP-Net enhances the classical U-Net architecture using a Group Pyramid Attention (GPA) module, which dynamically emphasizes discriminative features at multiple scales through a Channel Pooling Attention (CPA) block and a Spatial Hierarchical Attention (SHA) block. The SUS strategy leverages the SAM to identify hard-to-segment pixels and reduce prediction uncertainty. By combining semantic feature learning with foundation model-assisted refinement, our framework achieves promising performance across five publicly available datasets (i.e., BUSI, CVC, ISIC2017, ISIC2018, and Synapse). Extensive experiments show that it consistently outperforms U-Net and its multiple variants (e.g., UNeXt and UTNet), achieving an average Dice score of 0.8815 and a 95% Hausdorff distance of 7.3867, while demonstrating robust generalization across multimodal medical images. The source code is publicly available at https://github.com/wmuLei/hsp-net.
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2. Enhancing weakly supervised 3D medical image segmentation through probabilistic-aware learning.
2. 通过概率感知学习增强弱监督三维医学图像分割PMID:日期:2026-05-233D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning. Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation. However, this approach heavily relies on labor-intensive and time-consuming fully annotated ground-truth labels, particularly for 3D volumes. To overcome this limitation, we propose a novel probabilistic-aware weakly supervised learning pipeline, specifically designed for 3D medical imaging. Our pipeline integrates three innovative components: a Probability-based Pseudo-Label Generation technique for synthesizing dense segmentation masks from sparse annotations, a Probabilistic Multi-head Self-Attention network for robust feature extraction within our Probabilistic Transformer Network, and a Probability-informed Segmentation Loss Function to enhance training with annotation confidence. Demonstrating significant advances, our approach not only rivals the performance of fully supervised methods but also surpasses existing weakly supervised methods in CT and MRI datasets, achieving up to 18.1% improvement in Dice scores for certain organs. The code is available at https://github.com/runminjiang/PW4MedSeg.
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3. LDM-Morph: Latent diffusion model guided deformable image registration.
3. LDM-Morph：潜在扩散模型引导的可变形图像配准PMID:日期:2025-12-29Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features to predict the deformations. However, the lack of semantic information in the learned features limits the registration performance. Furthermore, the similarity metric of the loss function is often evaluated only in the pixel space, which ignores the matching of high-level anatomical features and can lead to deformation folding. To address these issues, in this work, we proposed LDM-Morph, an unsupervised deformable registration algorithm for medical image registration. LDM-Morph integrated features extracted from the latent diffusion model (LDM) to enrich the semantic information. Additionally, a latent and global feature-based cross-attention module (LGCA) was designed to enhance the interaction of semantic information from LDM and global information from multi-head self-attention operations. Finally, a hierarchical metric was proposed to evaluate the similarity of image pairs in both the original pixel space and latent-feature space, enhancing topology preservation while improving registration accuracy. Extensive experiments on four public 2D cardiac image datasets, two 3D image datasets, show that the proposed LDM-Morph framework outperformed existing state-of-the-art CNNs-and Transformers-based registration methods regarding accuracy with comparable topology preservation and computational efficiency. Our code is publicly available at: https://github.com/wujiong-hub/LDM-Morph.
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4. Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection.
PMID:日期:2025-12-06Generic object detection is a category-independent task that relies on accurate modeling of objectness. We show that for accurate semantic analysis, the network needs to learn all object-level predictions that appear at any stage of learning, including the predefined ground truth (GT) objects and the ambiguous decoy objects that the network misidentifies as foreground. Yet, most relevant models focused mainly on improving the learning of the GT objects. A few methods that consider decoy objects utilize loss functions that only focus on the single-response, i.e., the loss response of a single ambiguous pixel, and thus do not benefit from the wealth of information that an object-level ambiguity learning design can provide. Inspired by the human visual system, which first discerns the boundaries of ambiguous regions before delving into the semantic meaning, we propose a novel loss function, Spatial Coherence Loss (SCLoss), that incorporates the mutual response between adjacent pixels into the widely-used single-response loss functions. We demonstrate that the proposed SCLoss can gradually learn the ambiguous regions by detecting and emphasizing their boundaries in a self-adaptive manner. Through comprehensive experiments, we demonstrate that replacing popular loss functions with SCLoss can improve the performance of current state-of-the-art (SOTA) salient or camouflaged object detection (SOD or COD) models. We also demonstrate that combining SCLoss with other loss functions can further improve performance and result in SOTA outcomes for different applications. The codeis available at https://github.com/Zyun-Y/SCLoss.
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5. A Variational Bayesian Approach for Multimodal Multi-instance Classification.
PMID:日期:2025-11-28In multiple instance learning (MIL), objects are represented by bags of instances. Each instance shares the same feature set but has unique feature values. MIL aims to train models that predict bag-level outcomes based on features of these instances, making it a weakly supervised approach due to the lack of instance-level labels. While MIL methods focusing on binary classification are abundant, they often cannot identify which specific instances drive bag labels and have limited or little interpretability. Xiong et al. (2024) introduced MICProB, a Bayesian multiple instance classification (MIC) method that addresses these issues. However, MICProB is computationally intensive and best suited for unimodal instances. To overcome these limitations, we propose vMMIC, a novel variational Bayesian approach for multimodal MIC. vMMIC handles diverse instance types and significantly improves computational efficiency by leveraging Bayesian variational inference, coupled with the data augmentation technique. We benchmark vMMIC against MICProB and many other commonly used MIC approaches on both simulated and real-world data. Results demonstrate vMMIC's superior performance, faster computation, and improved interpretability.
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6. Variable Priority for Unsupervised Variable Selection.
6. 无监督变量选择的变量优先级PMID:日期:2025-11-24In unsupervised settings where labeled data is unavailable, identifying informative features is both challenging and essential. Although numerous methods for unsupervised feature selection have been proposed, significant opportunities for improvement remain. This paper introduces a new method that extends the supervised Variable Priority (VarPro) framework to the unsupervised domain. The central idea is to recast feature selection as a collection of localized two-class classification problems, where class labels are defined by membership in regions derived using decision tree rules and their corresponding releases. This transformation introduces a form of implicit supervision without requiring outcome labels and is combined with lasso-based regression to encourage sparsity and mitigate noise in high-dimensional settings. Extensive experiments on synthetic benchmarks demonstrate consistent improvements over existing methods across a range of latent, correlated, and clustered scenarios. Real-world validation in biological and image data further confirms the method's effectiveness, including recovery of known cancer-associated genes and improved clustering in lung cancer subtyping.
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7. A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.
PMID:日期:2025-08-26Neurobrain science provides the motivation for research on causal modeling. The existing causal discovery methods have shown promising results in effective connectivity network analysis, however, they often overlook the dynamics of causality, in addition to the incorporation of spatio-temporal information in data. Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain. To learn dynamic causality, we propose a deep spatio-temporal fusion architecture, which employs a dynamic causal deep encoder to incorporate spatio-temporal information into dynamic causality modeling, and a dynamic causal deep decoder to verify the discovered causality. The effectiveness of the proposed method is first illustrated with simulated data. Then, experimental results from Philadelphia Neurodevelopmental Cohort (PNC) demonstrate the superiority of the proposed method in inferring dECNs, which reveal the dynamic evolution of directed flow between brain regions. The analysis shows the difference of dECNs between young adults and children. Specifically, the directed brain functional networks transit from fluctuating undifferentiated systems to more stable specialized networks as one grows. This observation provides further evidence on the modularization and adaptation of brain networks during development, leading to higher cognitive abilities observed in young adults.
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8. Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.
8. 医学图像分割采用双解码器互教与均值教师框架PMID:日期:2025-07-21Accurate segmentation of medical images is essential for many clinical applications and is now typically achieved by training deep learning models on large annotated datasets. However, acquiring sufficient labeled images remains challenging, as pixel-level manual annotations are highly time-consuming. To substantially reduce the manual effort, we developed a novel semi-supervised segmentation method, termed dual-decoder mutual teaching (DDMT), which incorporates a smoothed exponential moving average (sEMA) scheme and a shape consistency constraint (SCC) scheme into the classical mean teacher (MT) framework. The sEMA scheme enhances the stability of the student and teacher models during training, while the SCC scheme ensures consistent learning of shape characteristics across the two different decoders within each model. With these two innovative components, DDMT achieves promising segmentation performance when trained on limited labeled images and abundant unlabeled images. Experiments on public datasets for left atrium, pancreas, and optic disc segmentation demonstrated that DDMT consistently outperforms several state-of-the-art semi-supervised learning (SSL) methods (e.g., MT, UAMT, DTC, and MCNet) across varying proportions of labeled images. The source code is publicly available at https://github.com/wmuLei/ddmt.
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9. Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms.
9. 有创冠状动脉造影中冠状动脉语义标记的多图图匹配PMID:日期:2025-06-10Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical information. However, deep learning-based methods encounter challenges in generating semantic labels for arterial segments, primarily due to the morphological similarity between arterial branches and varying anatomy of arterial system between different projection view angles and patients. To address this challenge, we model the vascular tree as a graph and propose a multi-graph matching (MGM) algorithm for coronary artery semantic labeling. The MGM algorithm assesses the similarity between arterials in multiple vascular tree graphs, considering the cycle consistency between each pair of graphs. As a result, the unannotated arterial segments are appropriately labeled by matching them with annotated segments. Through the incorporation of anatomical graph structure, radiomics features, and semantic mapping, the proposed MGM model achieves an impressive accuracy of 0.9471 for coronary artery semantic labeling using our multi-site dataset with 718 ICAs. With the semantic labeled arteries, an overall accuracy of 0.9155 was achieved for stenosis detection. The proposed MGM presents a novel tool for coronary artery analysis using multiple ICA-derived graphs, offering valuable insights into vascular health and pathology.
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10. A graph transformer-based foundation model for brain functional connectivity network.
10. 基于图变换器的脑功能连接网络基础模型PMID:日期:2025-06-10Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN). Our graph transformer model integrates node and edge embeddings to extract FCN features and adapts flexibly to classification, regression, and clustering via task-specific adapters. We validate the model on fMRI data from 10,718 subjects across multiple tasks: gender classification, mental disorder classification (distinguishing schizophrenia or autism from healthy population), brain age prediction, and depressive and anxiety disorder biotyping. Compared to 14 competing methods, our model consistently outperforms them. Moreover, it facilitates biomarker discovery by identifying task-specific FC patterns. In summary, we present a novel, versatile foundation model for FCN that advances neuroimaging research through scalable and interpretable analysis.