IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-04 DOI: 10.1007/s10489-025-06408-2

Xiang Li, Wei Liu, Wei Tang, Jixiang Guo

{"title":"Dense image-mask attention-guided transformer network for jaw lesions classification and segmentation in dental cone-beam computed tomography images","authors":"Xiang Li, Wei Liu, Wei Tang, Jixiang Guo","doi":"10.1007/s10489-025-06408-2","DOIUrl":"10.1007/s10489-025-06408-2","url":null,"abstract":"<div><p>Automatic segmentation and classification of jaw lesions from cone-beam computed tomography (CBCT) images are crucial in computer-assisted diagnosis and treatment planning for oral and maxillofacial (OMF) surgery. However, the evolutionary nature of jaw lesions and their morphological diversity pose significant challenges to both segmentation and classification tasks. Although existing deep learning-based works have achieved promising results on segmentation and classification of other types of lesions, they often consider the two tasks separately, thereby overlooking the strong guidance that lesion masks can provide in determining lesion categories. In this manuscript, we propose a dense image-mask attention-guided transformer network for end-to-end jaw lesions classification and segmentation in 3D CBCT images based on a multi-task learning (MTL) architecture. Specifically, we design multi-dimension attention (MDA) and multi-scale attention (MSA) modules to incorporate dense features from different dimensions and scales, explicitly enhancing the guidance of lesion segmentation for classification decisions. Furthermore, to effectively encode long-term contextual information, we employ a transformer as the classification decoder and design a 3D positional embedding method to preserve the 3D positional information of sequential feature inputs for the transformer. Finally, we design a task merge module that employs a per-lesion inference strategy to assign a category to each lesion instance. A large in-house dataset consisting of 358 CBCT scans with five types of jaw lesions is constructed to evaluate the proposed method. The experimental results show a binary segmentation DICE score of 90%, a mean classification accuracy of 89.23%, and a multi-class segmentation DICE score of 79.06%, surpassing many state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

E2E-QoE Oriented Framework: An Alternative Solution For Collaborative Optimization

IF 11.2 2区计算机科学

IEEE Communications Magazine Pub Date : 2025-03-04 DOI: 10.1109/mcom.005.2400299

Lei Ji, Jing Qian, Hao Wang

引用次数: 0

A General Framework for Probabilistic Relay Selection in Asymmetric Buffer-Aided Cooperative Relaying Systems

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2025-03-04 DOI: 10.1109/tcomm.2025.3547795

Peng Xu, Chenghong Luo, Chong Huang, Gaojie Chen, Yuanzhi He, Yong Li, Kai-Kit Wong

引用次数: 0

Design of a Dual Index PPM-DCSK Transceiver for Covert Wireless Communications

IF 8.3 2区计算机科学

IEEE Transactions on Communications Pub Date : 2025-03-04 DOI: 10.1109/tcomm.2025.3547768

Yonghao Mu, Lin Zhang, Zuwei Chen, Jieheng Zheng, Zhiqiang Wu

引用次数: 0

Network-Controlled Repeater Aided Time-Sensitive Communications in Urban Vehicular Networks

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2025-03-04 DOI: 10.1109/lwc.2025.3547810

Kai Dong, Hao Yu, Tarik Taleb, Aydin Sezgin, Sergiy A. Vorobyov

引用次数: 0

Discounted Inverse Reinforcement Learning for Linear Quadratic Control

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-03-04 DOI: 10.1109/tcyb.2025.3540967

Han Wu, Qinglei Hu, Jianying Zheng, Fei Dong, Zhenchao Ouyang, Dongyu Li

引用次数: 0

GraphLoc: Enhancing Fingerprint-Based Localization With Graph Representation Learning

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-03-04 DOI: 10.1109/jiot.2025.3547422

Lingyan Zhang, Yuanfeng Qiu, Shaohua Wu, Tingting Zhang, Qinyu Zhang

引用次数: 0

Adaptive Prescribed-Time Output Tracking of Clustered Uncertain Euler-Lagrange Systems: A Predefined-Track Containment Control Method

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-03-04 DOI: 10.1109/jiot.2025.3547746

Yanpeng Shi, Zhinan Peng, Yiqun Kuang, Yang Zhao, Jiangping Hu, Bijoy K. Ghosh

引用次数: 0

Evaluation of traditional machine learning algorithms for featuring educational exercises

IF 3.4 2区计算机科学

Applied Intelligence Pub Date : 2025-03-04 DOI: 10.1007/s10489-025-06386-5

Alberto Jiménez-Macías, Pedro J. Muñoz-Merino, Pedro Manuel Moreno-Marcos, Carlos Delgado Kloos

{"title":"Evaluation of traditional machine learning algorithms for featuring educational exercises","authors":"Alberto Jiménez-Macías, Pedro J. Muñoz-Merino, Pedro Manuel Moreno-Marcos, Carlos Delgado Kloos","doi":"10.1007/s10489-025-06386-5","DOIUrl":"10.1007/s10489-025-06386-5","url":null,"abstract":"<div><p>Artificial intelligence (AI) algorithms are important in educational environments, and the use of machine learning algorithms to evaluate and improve the quality of education. Previous studies have individually analyzed algorithms to estimate item characteristics, such as grade, number of attempts, and time from student interactions. By contrast, this study integrated all three characteristics to discern the relationships between attempts, time, and performance in educational exercises. We analyzed 15 educational assessments using different machine learning algorithms, specifically 12 for regression and eight for classification, with different hyperparameters. This study used real student interaction data from Zenodo.org, encompassing over 150 interactions per exercise, to predict grades and to improve our understanding of student performance. The results show that, in regression, the Bayesian ridge regression and random forest regression algorithms obtained the best results, and for the classification algorithms, Random Forest and Nearest Neighbors stood out. Most exercises in both scenarios involved more than 150 student interactions. Furthermore, the absence of a pattern in the variables contributes to suboptimal outcomes in some exercises. The information provided makes it more efficient to enhance the design of educational exercises.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10489-025-06386-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

引用次数: 0

Performance-Barrier Event-Triggered PDE Control of Traffic Flow

IF 6.8 1区计算机科学

IEEE Transactions on Automatic Control Pub Date : 2025-03-04 DOI: 10.1109/tac.2025.3547958

Peihan Zhang, Bhathiya Rathnayake, Mamadou Diagne, Miroslav Krstic

引用次数: 0

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