Image and Vision Computing最新文献_第9页

Automated recognition of humerus anomalies with convolutional neural networks 基于卷积神经网络的肱骨异常自动识别

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-01 DOI: 10.1016/j.imavis.2025.105799

Gea Viozzi , Fabio Persia , Daniela D’Auria

{"title":"Automated recognition of humerus anomalies with convolutional neural networks","authors":"Gea Viozzi , Fabio Persia , Daniela D’Auria","doi":"10.1016/j.imavis.2025.105799","DOIUrl":"10.1016/j.imavis.2025.105799","url":null,"abstract":"<div><div>Humerus anomalies are a problem that requires rapid and accurate diagnosis to ensure immediate and efficient treatment. In this context, the main goal of this paper is to develop and analyze well-known Convolutional Neural Network models for the automatic recognition of humeral fractures, with the aim of proposing a useful tool for healthcare personnel. Specifically, three distinct architectures were implemented and compared: a three-layer untrained neural network, a network based on the ResNet18 architecture and one based on the DenseNet121 model, both of which were trained. The performance analysis highlighted a trade-off between accuracy and generalization ability, showing better accuracy in the pre-trained models - in particular, the DenseNet121 model achieved optimal accuracy across multiple runs of 85%. - which however proved more prone to suffer from overfitting compared to the non-pre-trained model. As a result, this study aims to propose the integration of deep learning tools in medical practice, laying important foundations for future developments, with the hope of improving the efficiency and accuracy of orthopedic diagnoses.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105799"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive survey on magnetic resonance image reconstruction 磁共振图像重建技术综述

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.imavis.2025.105832

Xiaoyan Kui , Zijie Fan , Zexin Ji , Qinsong Li , Chengtao Liu , Weixin Si , Beiji Zou

引用次数: 0

DGFMamba: Model fine-tuning based on bidirectional state space for domain generalization semantic segmentation DGFMamba：基于双向状态空间的领域泛化语义分割模型微调

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-10-31 DOI: 10.1016/j.imavis.2025.105800

Yongchao Qiao , Ya’nan Guan , Zhiyou Wang , Jingmin Yang , Wenyuan Yang

{"title":"DGFMamba: Model fine-tuning based on bidirectional state space for domain generalization semantic segmentation","authors":"Yongchao Qiao , Ya’nan Guan , Zhiyou Wang , Jingmin Yang , Wenyuan Yang","doi":"10.1016/j.imavis.2025.105800","DOIUrl":"10.1016/j.imavis.2025.105800","url":null,"abstract":"<div><div>Compared with traditional semantic segmentation, Domain Generalization Semantic Segmentation (DGSS) focuses more on improving the generalization of models in unseen domains. Existing methods are mainly based on Transformers and convolutional neural networks, which have limited receptive fields and high complexity. Mamba, as a new state-space model, can solve these problems well. Nevertheless, the problems of hidden states and learning domain-invariant semantic features make it difficult to apply to DGSS. In this paper, we propose a model fine-tuning method named DGFMamba, which introduces Hidden State Fine-tuning Tokens (HSFT) and Feature-level Bidirectional Selective Scan Module (FBSSM) to improve the feature maps. HSFT, which consists of channel tokens and feature tokens, can perform local forgetting on feature maps. Feature-level embedding allows feature maps to be input to FBSSM with single pixels as vectors. FBSSM obtains contextual information from both forward and reverse directions, with reverse information serving as a complement to forward information. To further reduce the trainable parameters of the model, the parameters of FBSSM and MLP at each layer are shared. DGFMamba achieves promising results in experiments with different settings. This also demonstrates the effectiveness of applying state-space models to model fine-tuning. The average mIoU under the GTAV<span><math><mo>→</mo></math></span>scapes+BDD100K+Mapillary setting is 64.4%. The average mIoU under the GTAV+Synthia<span><math><mo>→</mo></math></span>Cityscapes+BDD100K+Mapillary setting is 65.8%. It is worth noting that DGFMamba only adds an additional 0.5% of trainable parameters. The code is available at <span><span>https://github.com/xiaoxia0722/DGFMamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105800"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DBaP-net: Deep network for image defogging based on physical properties prior DBaP-net：基于物理属性先验的图像去雾深度网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-10-31 DOI: 10.1016/j.imavis.2025.105803

Bo Yu , Hanting Wei , Chenghong Zhang , Wei Wang

{"title":"DBaP-net: Deep network for image defogging based on physical properties prior","authors":"Bo Yu , Hanting Wei , Chenghong Zhang , Wei Wang","doi":"10.1016/j.imavis.2025.105803","DOIUrl":"10.1016/j.imavis.2025.105803","url":null,"abstract":"<div><div>In the realm of computer vision, high-quality image information serves as the foundation for downstream tasks. Nevertheless, elements such as foggy weather, suboptimal lighting circumstances, and atmospheric impurities frequently deteriorate image quality, posing a considerable research challenge in effectively restoring these low-quality images. Existing defogging approaches mainly rely on constraints and physical priors; however, they have demonstrated limited efficacy, especially when dealing with extensive fog-affected areas. To tackle this issue, a deep trainable de-fog network named DBaP-net is proposed in this paper. By leveraging convolutional neural networks, this network integrates diverse filters to extract physical priors from images. Through the construction of a sophisticated deep network architecture, DBaP-net precisely estimates the transmission map and efficiently facilitates the restoration of haze-free images. Additionally, we design a spatial transformation layer customized for physical prior features and adopt a multi-kernel fusion extraction technique to further enhance the model’s feature extraction capabilities and spatial adaptability, thereby laying a solid foundation for subsequent visual tasks. Experimental validation indicates that DBaP-net not only effectively eliminates haze from images but also significantly enhances their overall quality. In both quantitative and qualitative evaluations, DBaP-net surpasses other comparison algorithms in terms of efficiency and usability. As a result, this study offers a novel solution to the image defogging problem within computer vision frameworks, enabling the precise restoration of low-quality images and providing robust support for research endeavors and downstream applications in related fields.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105803"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145428873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MTGT: Multiscale Text Feature-Guided Transformer in medical image segmentation MTGT：医学图像分割中的多尺度文本特征引导变换

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-24 DOI: 10.1016/j.imavis.2025.105846

Longxuan Zhao , Tao Wang , Xinlin Zhang , Yuanbin Chen , Ence Yang , Tong Tong

{"title":"MTGT: Multiscale Text Feature-Guided Transformer in medical image segmentation","authors":"Longxuan Zhao , Tao Wang , Xinlin Zhang , Yuanbin Chen , Ence Yang , Tong Tong","doi":"10.1016/j.imavis.2025.105846","DOIUrl":"10.1016/j.imavis.2025.105846","url":null,"abstract":"<div><div>To assist in the localization and characterization of diseases for enhanced diagnostic precision by clinical practitioners, the acquisition of high-quality annotations is recognized as both crucial and costly. The effectiveness of existing medical image segmentation models is notably hampered by their reliance on high-quality image inputs. It is identified that textual annotations for datasets can serve as a compensatory measure for deficiencies in image quality. In response to these challenges, a novel transformer, guided by multi-scale text features, is proposed. The proposed approach extracts multi-scale textual information alongside multimodal data from images, aiming at improving medical image segmentation. For the purpose of effectively integrating text annotations to mitigate the impact of image quality shortcomings, a multi-scale text processing module is introduced. This module is engineered to enhance the discernment of differential information through the technique of pyramid concatenation. Moreover, a new attention module is proposed, specifically devised to rectify the issue of inadequate focus on local features by the method of parallel stacking. When compared to contemporary models that utilize either solely image data or a combination of image–text data, the segmentation performance of the proposed model is demonstrated to be superior. The source code is publicly available at <span><span>https://github.com/zlxokok/MTGT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105846"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Density-aware global–local attention network for point cloud segmentation 基于密度感知的点云分割全局-局部关注网络

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-08 DOI: 10.1016/j.imavis.2025.105822

Chade Li , Pengju Zhang , Jiaming Zhang , Yihong Wu

{"title":"Density-aware global–local attention network for point cloud segmentation","authors":"Chade Li , Pengju Zhang , Jiaming Zhang , Yihong Wu","doi":"10.1016/j.imavis.2025.105822","DOIUrl":"10.1016/j.imavis.2025.105822","url":null,"abstract":"<div><div>3D point cloud segmentation has a wide range of applications in areas such as autonomous driving, augmented reality, virtual reality and digital twins. The point cloud data collected in real scenes often contain small objects and categories with small sample sizes, which are difficult to handle by existing networks. In this regard, we propose a point cloud segmentation network that fuses local attention based on density perception with global attention. The core idea is to increase the effective receptive field of each point while reducing the loss of information about small objects in dense areas. Specifically, we divide different sized windows for local areas with different densities to compute attention within the window. Furthermore, we consider each local area as an independent token for the global attention of the entire input. A category-response loss is also proposed to balance the processing of different categories and sizes of objects. In particular, we set up an additional fully connected layer in the middle of the network for prediction of the presence of object categories, and construct a binary cross-entropy loss to respond to the presence of categories in the scene. In experiments, our method achieves competitive results in semantic segmentation and part segmentation tasks on several publicly available datasets. Experiments on point cloud data obtained from complex real-world scenes filled with tiny objects also validate the strong segmentation capability of our method for small objects as well as small sample categories.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105822"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion model-based imbalanced diabetic retinal image classification 基于扩散模型的糖尿病视网膜不平衡图像分类

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-01 DOI: 10.1016/j.imavis.2025.105805

Yu Chen , Liu Yang , Jun Long , TingBo Bao

{"title":"Diffusion model-based imbalanced diabetic retinal image classification","authors":"Yu Chen , Liu Yang , Jun Long , TingBo Bao","doi":"10.1016/j.imavis.2025.105805","DOIUrl":"10.1016/j.imavis.2025.105805","url":null,"abstract":"<div><div>Since the release of Denoising Diffusion Probabilistic Models by Google in 2020, diffusion models have gradually emerged as a new research focus in generative modeling. However, in the task of diabetic retinopathy image classification, conventional convolutional neural network methods, although capable of achieving high accuracy, generally lack interpretability and thus fail to meet the transparency requirements of clinical diagnosis. To address this issue, a novel denoising diffusion framework named TCG-DiffDRC is proposed for diabetic retinopathy classification. An innovative triple-granularity conditional guidance strategy is introduced, in which three independent branches are fused. The global feature branch employs an improved ResNet-50 architecture with class activation mapping to generate global descriptors and capture macroscopic patterns. The local feature branch integrates multiple regions through a gated attention mechanism to identify local structures. The detail branch leverages an interpretable neural transformer with a multi-head attention mechanism to extract fine-grained lesion features. Furthermore, a dynamic guidance mechanism based on the correctness of an auxiliary classifier is incorporated during the diffusion reconstruction process, while segmentation masks are embedded as a regularization term in the loss function to enhance structural consistency in lesion regions. Experimental results demonstrate that TCG-DiffDRC consistently outperforms state-of-the-art methods across three public datasets, including APTOS2019, Messidor, and IDRiD. On the APTOS2019 dataset, the proposed method achieves an accuracy of 86.7% and a Cohen’s Kappa of 75.8%, with improvements confirmed by statistical significance testing, thereby verifying the reliability of the model.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105805"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MedSetFeat++: An attention-enriched set feature framework for few-shot medical image classification MedSetFeat++：一个用于少镜头医学图像分类的注意力富集集特征框架

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-15 DOI: 10.1016/j.imavis.2025.105825

Ankit Kumar Titoriya, Maheshwari Prasad Singh, Amit Kumar Singh

{"title":"MedSetFeat++: An attention-enriched set feature framework for few-shot medical image classification","authors":"Ankit Kumar Titoriya, Maheshwari Prasad Singh, Amit Kumar Singh","doi":"10.1016/j.imavis.2025.105825","DOIUrl":"10.1016/j.imavis.2025.105825","url":null,"abstract":"<div><div>Few-shot learning (FSL) has emerged as a promising solution to address the challenge of limited annotated data in medical image classification. However, traditional FSL methods often extract features from only one convolutional layer. This limits their ability to capture detailed spatial, semantic, and contextual information, which is important for accurate classification in complex medical scenarios. To overcome these limitations, this study introduces MedSetFeat++, an improved set feature learning framework with enhanced attention mechanisms, tailored for few-shot medical image classification. It extends the SetFeat architecture by incorporating several key innovations. It uses a multi-head attention mechanism with projections at multiple scales for the query, key, and value, allowing for more detailed feature interactions across different levels. It also includes learnable positional embeddings to preserve spatial information. An adaptive head gating method is added to control the flow of attention in a dynamic way. Additionally, a Convolutional Block Attention Module (CBAM) based attention module is used to improve focus on the most relevant regions in the data. To evaluate the performance and generalization of MedSetFeat++, extensive experiments were conducted using three different medical imaging datasets: HAM10000, BreakHis at 400<span><math><mo>×</mo></math></span> magnification, and Kvasir. Under a 2-way 10-shot 15-query setting, the model achieves 92.17% accuracy on HAM10000, 70.89% on BreakHis, and 73.46% on Kvasir. The proposed model outperforms state-of-the-art methods in multiple 2-way classification tasks under 1-shot, 5-shot, and 10-shot settings. These results establish MedSetFeat++ as a strong and adaptable framework for improving performance in few-shot medical image classification.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105825"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HF-D-FINE: High-resolution features enhanced D-FINE for tiny object detection in UAV image HF-D-FINE：高分辨率功能增强的D-FINE，用于无人机图像中的微小物体检测

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.imavis.2025.105834

Qianhua Hu, Liantao Wang

{"title":"HF-D-FINE: High-resolution features enhanced D-FINE for tiny object detection in UAV image","authors":"Qianhua Hu, Liantao Wang","doi":"10.1016/j.imavis.2025.105834","DOIUrl":"10.1016/j.imavis.2025.105834","url":null,"abstract":"<div><div>Real-time detection in UAV-captured imagery remains a formidable challenge, primarily attributed to the inherent tension between high detection performance and strict computational economy. To address this dilemma, we introduce HF-D-FINE, a novel object-detection paradigm that builds upon the D-FINE architecture and comprises three effective innovations. The HF Hybrid Encoder alleviates the loss of fine-grained detail by selectively injecting high-resolution cues from the backbone’s feature pyramid into the encoder, thereby enriching the representation of minute instances. Complementarily, the CAF module performs cross-scale feature fusion by integrating channel-attentive mechanisms and dynamic upsampling, enabling more expressive interactions between multi-level semantics and spatial cues. Finally, Outer-SNWD introduces aspect ratio consistency penalty factor and auxiliary boxes based on the advantages of Shape-IoU and NWD, making it more suitable for tiny object detection tasks. Collectively, these components substantially elevate tiny object detection accuracy while preserving low computational overhead. Extensive experiments on the widely-adopted aerial benchmarks VisDrone, AI-TOD, and UAVDT demonstrate that HF-D-FINE achieves superior accuracy with a tiny increase in FLOPs. In the VisDrone dataset, the AP value is increased by 3.2% compared with D-FINE-S, and the AP<sub>50</sub> value is increased by 4.3%, confirming its efficacy and superiority for tiny object detection in UAV image.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105834"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Object Localization driven by self-supervised foundation models: A comprehensive review 基于自监督基础模型驱动的无监督对象定位：综述

IF 4.2 3区计算机科学

Image and Vision Computing Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.imavis.2025.105807

Sotirios Papadopoulos , Emmanouil Patsiouras , Konstantinos Ioannidis , Stefanos Vrochidis , Ioannis Kompatsiaris , Ioannis Patras

{"title":"Unsupervised Object Localization driven by self-supervised foundation models: A comprehensive review","authors":"Sotirios Papadopoulos , Emmanouil Patsiouras , Konstantinos Ioannidis , Stefanos Vrochidis , Ioannis Kompatsiaris , Ioannis Patras","doi":"10.1016/j.imavis.2025.105807","DOIUrl":"10.1016/j.imavis.2025.105807","url":null,"abstract":"<div><div>Object localization is a fundamental task in computer vision that traditionally requires labeled datasets for accurate results. Recent progress in self-supervised learning has enabled unsupervised object localization, reducing reliance on manual annotations. Unlike supervised encoders, which depend on annotated training data, self-supervised encoders learn semantic representations directly from large collections of unlabeled images. This makes them the natural foundation for unsupervised object localization, as they capture object-relevant features while eliminating the need for costly manual labels. These encoders produce semantically coherent patch embeddings. Grouping these embeddings reveals sets of patches that correspond to objects in an image. These patch sets can be converted into object masks or bounding boxes, enabling tasks such as single-object discovery, multi-object detection, and instance segmentation. By applying off-line mask clustering or using pre-trained vision-language models, unsupervised localization methods can assign semantic labels to discovered objects. This transforms initially class-agnostic objects (objects without class labels) into class-aware ones (objects with class labels), aligning these tasks with their supervised counterparts. This paper provides a structured review of unsupervised object localization methods in both class-agnostic and class-aware settings. In contrast, previous surveys have focused only on class-agnostic localization. We discuss state-of-the-art object discovery strategies based on self-supervised features and provide a detailed comparison of experimental results across a wide range of tasks, datasets, and evaluation metrics.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"165 ","pages":"Article 105807"},"PeriodicalIF":4.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0