The structural and functional brain alternations in tobacco use disorder: a systematic review and meta-analysis by Longyao Ma, Longyao Ma, Qiuying Tao, Qiuying Tao, Jinghan Dang, Jinghan Dang, Jieping Sun, Jieping Sun, Xiaoyu Niu, Xiaoyu Niu, Mengzhe Zhang, Mengzhe Zhang, Yimeng Kang, Yimeng Kang, Weijian Wang, Weijian Wang, Jingliang Cheng, Jingliang Cheng, Yong Zhang, Yong Zhang

“…Consequently, it is important to utilize relevant coordinate data for a comprehensive meta-analysis to identify the shared patterns of structural, functional, and multimodal alternations in TUD.MethodThe present study conducted a systematic retrieval of studies published on PubMed, Web of Science, and Scopus from January 1, 2010, to December 12, 2023, to identify studies on voxel-based morphometry (VBM) and resting-state functional magnetic resonance imaging (rs-fMRI) for TUD. …”
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Artificial Intelligence Approach in Machine Learning-Based Modeling and Networking of the Coronavirus Pathogenesis Pathway by Shihori Tanabe, Sabina Quader, Ryuichi Ono, Hiroyoshi Y. Tanaka, Akihisa Yamamoto, Motohiro Kojima, Edward J. Perkins, Horacio Cabral

“…Data on coronaviral infection held in a database were analyzed with Ingenuity Pathway Analysis (IPA), a network pathway analysis tool. …”
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Design and Research of Building Indoor Environment Monitoring System Relying on 3D Modeling and Image Processing by Chenguang Liu

“…In order to improve the monitoring effect of building indoor environment, this paper carries out indoor environment simulation through 3D modeling, uses image processing technology to collect environmental monitoring data, and proposes a routing algorithm applied in this environment. …”
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Transformers for Vision: A Survey on Innovative Methods for Computer Vision by Balamurugan Palanisamy, Vikas Hassija, Arpita Chatterjee, Arpita Mandal, Debanshi Chakraborty, Amit Pandey, G. S. S. Chalapathi, Dhruv Kumar

“…We delve into the theoretical adaptations required to apply transformers to visual data, including image tokenization and the integration of positional embeddings. …”
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Legal regulation of the genesis of digital identity by O. A. Stepanov, M. M. Stepanov

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Morphological analysis and functional connectivity of the insular in patients with dysphagia after cerebral infarction based on resting-state fMRI by Ming Guo, Bingjie Li, Jun Zhao, Chen Bai, Weiyong Yu, Hongxia Zhang, Haoyuan Li, Yongxue Yuan, Qingsu Zhang, Tong Zhang

“…Resting-state functional magnetic resonance imaging (rs-fMRI) and high-resolution T1-weighted structural imaging data were acquired. …”
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Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer by Xinyu Qi

“…A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. …”
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Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence by I.V. Belkin, A.A. Abramenko, V.D. Bezuglyi, D.A. Yudin

“…A novel localization approach for mobile ground robot, which successfully combines conventional computer vision techniques, neural network based image analysis and numerical optimization, is proposed. …”
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A multi-filter deep transfer learning framework for image-based autism spectrum disorder detection by Rodrigo Colnago Contreras, Monique Simplicio Viana, Victor José Souza Bernardino, Francisco Lledo dos Santos, Önsen Toygar, Rodrigo Capobianco Guido

“…This study proposes a novel framework that integrates data augmentation, multi-filtering routines, histogram equalization, and a two-stage dimensionality reduction process to enrich the representation in pre-trained and frozen deep learning neural network models applied to image pattern recognition. …”
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MedFusion-TransNet: multi-modal fusion via transformer for enhanced medical image segmentation by Jianfei Sun

“…IntroductionMedical image segmentation is essential for analyzing medical data, improving diagnostics, treatment planning, and research. …”
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Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition by Stanislav Jiricek, Stanislav Jiricek, Stanislav Jiricek, Vlastimil Koudelka, Dante Mantini, Radek Marecek, Jaroslav Hlinka, Jaroslav Hlinka

“…However, we did not observe a statistically significant relationship between the EEG spatio-spectral patterns and the classical fMRI BOLD resting-state networks (as identified through independent component analysis), tested as the similarity between their temporal synchronization and spatial overlap. …”
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Skin Parameter Map Retrieval from a Dedicated Multispectral Imaging System Applied to Dermatology/Cosmetology by Romuald Jolivot, Yannick Benezeth, Franck Marzani

“…It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. …”
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Resting-state functional magnetic resonance imaging in bipolar affective disorder. A systematized umbrella review by K. V. Markin

“…Furthermore, a comparative analysis of possible methods of quantitative fMRI data synthesis has been conducted for the first time in Russia, which is a valuable contribution to the field.…”
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Improving and simulating urban landscape image recognition using combination optimization and fuzzy K-means algorithm by Lihua Yang, Yuhui Zheng

“…The research introduces a novel method combining a Combination Optimization (CO) strategy with a Fuzzy K-Means (FKM) clustering algorithm to address the challenges and achieve superior urban data analysis performance. CO specifically integrates the genetic algorithm (GA) to efficiently search for the optimal subset of features that maximize the performance of a convolutional neural network (CNN) based on extracted features. …”
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Unsupervised SAR Image Change Detection Based on Curvelet Fusion and Local Patch Similarity Information Clustering by Yuhao Huang, Zhihui Xin, Guisheng Liao, Penghui Huang, Guangyu Hou, Rui Zou

“…At the same time, the existing unsupervised deep learning-based methods relying on the pseudo labels may lead to a low-performance network. These methods are high data-dependent. To this end, we propose a novel unsupervised change detection method based on curvelet fusion and local patch similarity information clustering (CF-LPSICM). …”
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Exploring BIM technology development using patent citation analysis: USA case study by Kuo-Chan Weng, Jieh-Haur Chen, Rico Lee-Ting Cho, Hsi-Hsien Wei, Tzuyang Yu

“…The main path is composed of three streams: Stream I is used for the construction and application of 3D models, Stream II is used for the presentation of 3D images, and Stream III is used for the automatic analysis of architectural information. …”
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Accurate and lightweight oral cancer detection using SE-MobileViT on clinically validated image dataset by Md Firoz Kabir, Md Yousuf Ahmad, Roise Uddin, Martin Cordero, Shashi Kant

“…The original dataset consisted of 131 images (87 cancerous and 44 non-cancerous). To address class imbalance and enhance model generalizability, data augmentation techniques were employed, expanding the dataset to 981 images with equal distribution across both classes. …”
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The Campi Flegrei Blind Test: Evaluating the Imaging Capability of Local Earthquake Tomography in a Volcanic Area by E. Priolo, L. Lovisa, A. Zollo, G. Böhm, L. D'Auria, S. Gautier, F. Gentile, P. Klin, D. Latorre, A. Michelini, T. Vanorio, J. Virieux

“…In order to analyse the potential of the recorded data in terms of tomographic imaging, a blind test was recently set up and carried out in the framework of a research project. …”
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Disrupted functional connectivity between visual and emotional networks in psychosis risk syndromes through representational similarity analysis by Xi Ruan, Lang Zhang, Mingjun Duan, Dezhong Yao, Dezhong Yao, Cheng Luo, Cheng Luo, Cheng Luo, Hui He

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Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…<b>Conclusions</b>: Based on CT imaging data from 1066 patients, this study systematically constructed predictive models for pathological subtypes, T staging, and N staging of esophageal cancer. …”
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