International Journal of Imaging Systems and Technology最新文献

{"title":"Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm","authors":"Bijoyeta Roy,&nbsp;Mousumi Gupta,&nbsp;Bidyut Krishna Goswami","doi":"10.1002/ima.23179","DOIUrl":"https://doi.org/10.1002/ima.23179","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal adenocarcinoma, the most prevalent form of colon cancer, originates in the glandular structures of the intestines, presenting histopathological abnormalities in affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success in segmenting glands in benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims to develop a robust learning algorithm using a convolutional neural network (CNN) to segment glandular structures in colon histology images. The methodology employs a CNN based on the U-Net architecture, augmented by a weighted ensemble network that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, the segmented gland boundaries are refined using the watershed algorithm. Evaluation on the Warwick-QU dataset demonstrates promising results for the ensemble model, by achieving an F1 score of 0.928 and 0.913, object dice coefficient of 0.923 and 0.911, and Hausdorff distances of 38.97 and 33.76 on test sets A and B, respectively. These results are compared with outcomes from the GlaS challenge (MICCAI 2015) and existing research findings. Furthermore, our model is validated with a publicly available dataset named LC25000, and visual inspection reveals promising results, further validating the efficacy of our approach. The proposed ensemble methodology underscores the advantages of amalgamating diverse models, highlighting the potential of ensemble techniques to enhance segmentation tasks beyond individual model capabilities.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancement of Semantic Segmentation by Image-Level Fine-Tuning to Overcome Image Pattern Imbalance in HRCT of Diffuse Infiltrative Lung Diseases","authors":"Sungwon Ham,&nbsp;Beomhee Park,&nbsp;Jihye Yun,&nbsp;Sang Min Lee,&nbsp;Joon Beom Seo,&nbsp;Namkug Kim","doi":"10.1002/ima.23188","DOIUrl":"https://doi.org/10.1002/ima.23188","url":null,"abstract":"<div>\u0000 \u0000 <p>Diagnosing diffuse infiltrative lung diseases (DILD) using high-resolution computed tomography (HRCT) is challenging, even for expert radiologists, due to the complex and variable image patterns. Moreover, the imbalances among the six key DILD-related patterns—normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation—further complicate accurate segmentation and diagnosis. This study presents an enhanced U-Net-based segmentation technique aimed at addressing these challenges. The primary contribution of our work is the fine-tuning of the U-Net model using image-level labels from 92 HRCT images that include various types of DILDs, such as cryptogenic organizing pneumonia, usual interstitial pneumonia, and nonspecific interstitial pneumonia. This approach helps to correct the imbalance among image patterns, improving the model's ability to accurately differentiate between them. By employing semantic lung segmentation and patch-level machine learning, the fine-tuned model demonstrated improved agreement with radiologists' evaluations compared to conventional methods. This suggests a significant enhancement in both segmentation accuracy and inter-observer consistency. In conclusion, the fine-tuned U-Net model offers a more reliable tool for HRCT image segmentation, making it a valuable imaging biomarker for guiding treatment decisions in patients with DILD. By addressing the issue of pattern imbalances, our model significantly improves the accuracy of DILD diagnosis, which is crucial for effective patient care.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CafeNet: A Novel Multi-Scale Context Aggregation and Multi-Level Foreground Enhancement Network for Polyp Segmentation","authors":"Zhanlin Ji,&nbsp;Xiaoyu Li,&nbsp;Zhiwu Wang,&nbsp;Haiyang Zhang,&nbsp;Na Yuan,&nbsp;Xueji Zhang,&nbsp;Ivan Ganchev","doi":"10.1002/ima.23183","DOIUrl":"https://doi.org/10.1002/ima.23183","url":null,"abstract":"<p>The detection of polyps plays a significant role in colonoscopy examinations, cancer diagnosis, and early patient treatment. However, due to the diversity in the size, color, and shape of polyps, as well as the presence of low image contrast with the surrounding mucosa and fuzzy boundaries, precise polyp segmentation remains a challenging task. Furthermore, this task requires excellent real-time performance to promptly and efficiently present predictive results to doctors during colonoscopy examinations. To address these challenges, a novel neural network, called CafeNet, is proposed in this paper for rapid and accurate polyp segmentation. CafeNet utilizes newly designed multi-scale context aggregation (MCA) modules to adapt to the extensive variations in polyp morphology, covering small to large polyps by fusing simplified global contextual information and local information at different scales. Additionally, the proposed network utilizes newly designed multi-level foreground enhancement (MFE) modules to compute and extract differential features between adjacent layers and uses the prediction output from the adjacent lower-layer decoder as a guidance map to enhance the polyp information extracted by the upper-layer encoder, thereby improving the contrast between polyps and the background. The polyp segmentation performance of the proposed CafeNet network is evaluated on five benchmark public datasets using six evaluation metrics. Experimental results indicate that CafeNet outperforms the state-of-the-art networks, while also exhibiting the least parameter count along with excellent real-time operational speed.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automatic Measurement Method of the Tibial Deformity Angle on X-Ray Films Based on Deep Learning Keypoint Detection Network","authors":"Ning Zhao,&nbsp;Cheng Chang,&nbsp;Yuanyuan Liu,&nbsp;Xiao Li,&nbsp;Zicheng Song,&nbsp;Yue Guo,&nbsp;Jianwen Chen,&nbsp;Hao Sun","doi":"10.1002/ima.23190","DOIUrl":"https://doi.org/10.1002/ima.23190","url":null,"abstract":"<div>\u0000 \u0000 <p>In the clinical application of the parallel external fixator, medical practitioners are required to quantify deformity parameters to develop corrective strategies. However, manual measurement of deformity angles is a complex and time-consuming process that is susceptible to subjective factors, resulting in nonreproducible results. Accordingly, this study proposes an automatic measurement method based on deep learning, comprising three stages: tibial segment localization, tibial contour point detection, and deformity angle calculation. First, the Faster R-CNN object detection model, combined with ResNet50 and FPN as the backbone, was employed to achieve accurate localization of tibial segments under both occluded and nonoccluded conditions. Subsequently, a relative position constraint loss function was added, and ResNet101 was used as the backbone, resulting in an improved RTMPose keypoint detection model that achieved precise detection of tibial contour points. Ultimately, the bone axes of each tibial segment were determined based on the coordinates of the contour points, and the deformity angles were calculated. The enhanced keypoint detection model, Con_RTMPose, elevated the Percentage of Correct Keypoints (PCK) from 63.94% of the initial model to 87.17%, markedly augmenting keypoint localization precision. Compared to manual measurements conducted by medical professionals, the proposed methodology demonstrates an average error of 0.52°, a maximum error of 1.15°, and a standard deviation of 0.07, thereby satisfying the requisite accuracy standards for orthopedic assessments. The measurement time is approximately 12 s, whereas manual measurement requires about 15 min, greatly reducing the time required. Additionally, the stability of the models was verified through <i>K</i>-fold cross-validation experiments. The proposed method meets the accuracy requirements for orthopedic applications, provides objective and reproducible results, significantly reduces the workload of medical professionals, and greatly improves efficiency.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation Design of a Triple Antenna Combination for PET-MRI Imaging Compatible With 3, 7, and 11.74 T MRI Scanner","authors":"Daniel Hernandez,&nbsp;Taewoo Nam,&nbsp;Eunwoo Lee,&nbsp;Geun Bae Ko,&nbsp;Jae Sung Lee,&nbsp;Kyoung-Nam Kim","doi":"10.1002/ima.23191","DOIUrl":"https://doi.org/10.1002/ima.23191","url":null,"abstract":"<p>The use of electromagnetism and the design of antennas in the field of medical imaging have played important roles in clinical practice. Specifically, magnetic resonance imaging (MRI) utilizes transmission and reception antennas, or coils, that are tuned to specific frequencies depending on the strength of the main magnet. Clinical scanners operating at 3 Teslas (T) function at a frequency of 127 MHz, while research scanners at 7 T operate at 300 MHz. An 11.74 T scanner for human imaging, which is currently under development, will operate at a frequency of 500 MHz. MRI allows for the high-definition scanning of biological tissues, making it a valuable tool for enhancing images acquired with positron emission tomography (PET). PET is an imaging modality used to evaluate the metabolism of organs or cancers. With recent advancements in the development of portable PET systems that can be integrated into any MRI scanner, we propose the design based on electromagnetic simulations of a triple-tuned array of dipole antennas to operate at 127, 300, and 500 MHz. This array can be attached to the PET inset and used in 3, 7, or 11.74 T scanners.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting Segment Anything Model for 3D Brain Tumor Segmentation With Missing Modalities","authors":"Xiaoliang Lei,&nbsp;Xiaosheng Yu,&nbsp;Maocheng Bai,&nbsp;Jingsi Zhang,&nbsp;Chengdong Wu","doi":"10.1002/ima.23177","DOIUrl":"https://doi.org/10.1002/ima.23177","url":null,"abstract":"<div>\u0000 \u0000 <p>The problem of missing or unavailable magnetic resonance imaging modalities challenges clinical diagnosis and medical image analysis technology. Although the development of deep learning and the proposal of large models have improved medical analytics, this problem still needs to be better resolved.The purpose of this study was to efficiently adapt the Segment Anything Model, a two-dimensional visual foundation model trained on natural images, to address the challenge of brain tumor segmentation with missing modalities. We designed a twin network structure that processes missing and intact magnetic resonance imaging (MRI) modalities separately using shared parameters. It involved comparing the features of two network branches to minimize differences between the feature maps derived from them. We added a multimodal adapter before the image encoder and a spatial–depth adapter before the mask decoder to fine-tune the Segment Anything Model for brain tumor segmentation. The proposed method was evaluated using datasets provided by the MICCAI BraTS2021 Challenge. In terms of accuracy and robustness, the proposed method is better than existing solutions. The proposed method can segment brain tumors well under the missing modality condition.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142316943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Dual Cascaded Deep Theoretic Learning Approach for the Segmentation of the Brain Tumors in MRI Scans","authors":"Jinka Sreedhar,&nbsp;Suresh Dara,&nbsp;C. H. Srinivasulu,&nbsp;Butchi Raju Katari,&nbsp;Ahmed Alkhayyat,&nbsp;Ankit Vidyarthi,&nbsp;Mashael M. Alsulami","doi":"10.1002/ima.23186","DOIUrl":"https://doi.org/10.1002/ima.23186","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is crucial for diagnosis, treatment planning, and monitoring of patients with neurological disorders. This paper proposes an approach for brain tumor segmentation employing a cascaded architecture integrating L-Net and W-Net deep learning models. The proposed cascaded model leverages the strengths of U-Net as a baseline model to enhance the precision and robustness of the segmentation process. In the proposed framework, the L-Net excels in capturing the mask, while the W-Net focuses on fine-grained features and spatial information to discern complex tumor boundaries. The cascaded configuration allows for a seamless integration of these complementary models, enhancing the overall segmentation performance. To evaluate the proposed approach, extensive experiments were conducted on the datasets of BraTs and SMS Medical College comprising multi-modal MRI images. The experimental results demonstrate that the cascaded L-Net and W-Net model consistently outperforms individual models and other state-of-the-art segmentation methods. The performance metrics such as the Dice Similarity Coefficient value achieved indicate high segmentation accuracy, while Sensitivity and Specificity metrics showcase the model's ability to correctly identify tumor regions and exclude healthy tissues. Moreover, the low Hausdorff Distance values confirm the model's capability to accurately delineate tumor boundaries. In comparison with the existing methods, the proposed cascaded scheme leverages the strengths of each network, leading to superior performance compared to existing works of literature.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CasUNeXt: A Cascaded Transformer With Intra- and Inter-Scale Information for Medical Image Segmentation","authors":"Junding Sun,&nbsp;Xiaopeng Zheng,&nbsp;Xiaosheng Wu,&nbsp;Chaosheng Tang,&nbsp;Shuihua Wang,&nbsp;Yudong Zhang","doi":"10.1002/ima.23184","DOIUrl":"https://doi.org/10.1002/ima.23184","url":null,"abstract":"<p>Due to the Transformer's ability to capture long-range dependencies through Self-Attention, it has shown immense potential in medical image segmentation. However, it lacks the capability to model local relationships between pixels. Therefore, many previous approaches embedded the Transformer into the CNN encoder. However, current methods often fall short in modeling the relationships between multi-scale features, specifically the spatial correspondence between features at different scales. This limitation can result in the ineffective capture of scale differences for each object and the loss of features for small targets. Furthermore, due to the high complexity of the Transformer, it is challenging to integrate local and global information within the same scale effectively. To address these limitations, we propose a novel backbone network called CasUNeXt, which features three appealing design elements: (1) We use the idea of cascade to redesign the way CNN and Transformer are combined to enhance modeling the unique interrelationships between multi-scale information. (2) We design a Cascaded Scale-wise Transformer Module capable of cross-scale interactions. It not only strengthens feature extraction within a single scale but also models interactions between different scales. (3) We overhaul the multi-head Channel Attention mechanism to enable it to model context information in feature maps from multiple perspectives within the channel dimension. These design features collectively enable CasUNeXt to better integrate local and global information and capture relationships between multi-scale features, thereby improving the performance of medical image segmentation. Through experimental comparisons on various benchmark datasets, our CasUNeXt method exhibits outstanding performance in medical image segmentation tasks, surpassing the current state-of-the-art methods.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ConvNext Mixer-Based Encoder Decoder Method for Nuclei Segmentation in Histopathology Images","authors":"Hüseyin Firat,&nbsp;Hüseyin Üzen,&nbsp;Davut Hanbay,&nbsp;Abdulkadir Şengür","doi":"10.1002/ima.23181","DOIUrl":"https://doi.org/10.1002/ima.23181","url":null,"abstract":"<p>Histopathology, vital in diagnosing medical conditions, especially in cancer research, relies on analyzing histopathology images (HIs). Nuclei segmentation, a key task, involves precisely identifying cell nuclei boundaries. Manual segmentation by pathologists is time-consuming, prompting the need for robust automated methods. Challenges in segmentation arise from HI complexities, necessitating advanced techniques. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have transformed nuclei segmentation. This study emphasizes feature extraction, introducing the ConvNext Mixer-based Encoder-Decoder (CNM-ED) model. Unlike traditional CNN based models, the proposed CNM-ED model enables the extraction of spatial and long context features to address the inherent complexities of histopathology images. This method leverages a multi-path strategy using a traditional CNN architecture as well as different paths focused on obtaining customized long context features using the ConvNext Mixer block structure that combines ConvMixer and ConvNext blocks. The fusion of these diverse features in the final segmentation output enables improved accuracy and performance, surpassing existing state-of-the-art segmentation models. Moreover, our multi-level feature extraction strategy is more effective than models using self-attention mechanisms such as SwinUnet and TransUnet, which have been frequently used in recent years. Experimental studies were conducted using five different datasets (TNBC, MoNuSeg, CoNSeP, CPM17, and CryoNuSeg) to analyze the performance of the proposed CNM-ED model. Comparisons were made with various CNN based models in the literature using evaluation metrics such as accuracy, AJI, macro F1 score, macro intersection over union, macro precision, and macro recall. It was observed that the proposed CNM-ED model achieved highly successful results across all metrics. Through comparisons with state-art-of models from the literature, the proposed CNM-ED model stands out as a promising advancement in nuclei segmentation, addressing the intricacies of histopathological images. The model demonstrates enhanced diagnostic capabilities and holds the potential for significant progress in medical research.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142276595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Deformation Vector Field Generation With Diffusion Models and Mamba-Based Network for Registration Performance Enhancement","authors":"Zengan Huang,&nbsp;Shan Gao,&nbsp;Xiaxia Yu,&nbsp;Liangjia Zhu,&nbsp;Yi Gao","doi":"10.1002/ima.23171","DOIUrl":"https://doi.org/10.1002/ima.23171","url":null,"abstract":"<p>Recent advancements in deformable image registration (DIR) have seen the emergence of supervised and unsupervised deep learning techniques. However, supervised methods are limited by the quality of deformation vector fields (DVFs), while unsupervised approaches often yield suboptimal results due to their reliance on indirect dissimilarity metrics. Moreover, both methods struggle to effectively model long-range dependencies. This study proposes a novel DIR method that integrates the advantages of supervised and unsupervised learning and tackle issues related to long-range dependencies, thereby improving registration results. Specifically, we propose a DVF generation diffusion model to enhance DVFs diversity, which could be used to facilitate the integration of supervised and unsupervised learning approaches. This fusion allows the method to leverage the benefits of both paradigms. Furthermore, a multi-scale frequency-weighted denoising module is integrated to enhance DVFs generation quality and improve the registration accuracy. Additionally, we propose a novel MambaReg network that adeptly manages long-range dependencies, further optimizing registration outcomes. Experimental evaluation of four public data sets demonstrates that our method outperforms several state-of-the-art techniques based on either supervised or unsupervised learning. Qualitative and quantitative comparisons highlight the superior performance of our approach.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"34 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.23171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}