ROPGCViT: A Novel Explainable Vision Transformer for Retinopathy of Prematurity Diagnosis by Mustafa Yurdakul, Kubra Uyar, Sakir Tasdemir, Irfan Atabas

“…GCViT was enhanced with Squeeze-and-Excitation (SE) block and Residual Multilayer Perceptron (RMLP) structures to effectively learn local and global context information. …”
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Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model by Santosh Kumar Henge, Nikhil Reddy Viraati, Musaed Alhussein, Ajay Shriram Kushwaha, Khursheed Aurangzeb, Ravleen Singh

“…The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. …”
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Multi-scale Information Aggregation for Spoofing Detection by Changtao Li, Yi Wan, Feiran Yang, Jun Yang

“…In this paper, we propose a spoofing detection system built on SincNet and Deep Layer Aggregation (DLA), which leverages speech representations at different levels to distinguish synthetic speech. DLA is totally convolutional with an iterative tree-like structure. …”
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Fine-grained crop pest classification based on multi-scale feature fusion and mixed attention mechanisms by Yiheng Qian, Zhiyong Xiao, Zhaohong Deng

“…Additionally, a Transformer block is integrated to overcome the limitations of traditional convolutional approaches in capturing global contextual information. …”
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Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications by Yiheng Ding, Ziqiang Wei, Chaoyun Wang, Xinyue Li, Bingbing Li, Xueting Liu, Zhijie Fu, Hongwei Mo, Hong Zhang

“…Ophthalmic vessels are the only vascular structures that can be directly observed in vivo in a non-invasive manner. …”
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NGSTGAN: N-Gram Swin Transformer and Multi-Attention U-Net Discriminator for Efficient Multi-Spectral Remote Sensing Image Super-Resolution by Chao Zhan, Chunyang Wang, Bibo Lu, Wei Yang, Xian Zhang, Gaige Wang

“…Recent advancements in convolutional neural networks (CNNs) and Transformers have significantly improved RSISR performance due to their capabilities in local feature extraction and global modeling. …”
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PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction From High-Resolution Remote Sensing Images by Yaoteng Zhang, Julin Zhang, Guangshuai Wang, Jiwei Deng, Hui Sheng, Yasir Muhammad, Shiqing Wei

“…However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. …”
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Astronomical Image Superresolution Reconstruction with Deep Learning for Better Identification of Interacting Galaxies by Jiawei Miao, Liangping Tu, Hao Liu, Jian Zhao

“…To further improve visual quality and enhance the details of galaxy structures, we propose a dual-branch network structure combining convolutional neural networks (CNNs) and Transformer (DBCTNet), which leverages the local characteristics of CNNs to complement the global features of Transformer. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

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Frequency-Aware Learned Image Compression Using Channel-Wise Attention and Restormer by Hanwen Zhang, Cheolkon Jung, Xu Liu

“…However, learned image compression has a limitation of balancing global context and local texture because the global structure easily ignores local redundancy, especially for the non-repetitive textures, affecting the reconstruction performance. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Background/Objectives: Alzheimer’s disease (AD), a progressive neurodegenerative disorder, demands precise early diagnosis to enable timely interventions. Traditional convolutional neural networks (CNNs) and deep learning models often fail to effectively integrate localized brain changes with global connectivity patterns, limiting their efficacy in Alzheimer’s disease (AD) classification. …”
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Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin by Lifu Zheng, Hao Yang, Guichun Luo

“…The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). …”
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Joint Grid-Based Attention and Multilevel Feature Fusion for Landslide Recognition by Xinran Li, Tao Chen, Gang Liu, Jie Dou, Ruiqing Niu, Antonio Plaza

“…Landslide recognition (LR) is a fundamental task for disaster prevention and control. Convolutional neural networks (CNNs) and transformer architectures have been widely used for extracting landslide information. …”
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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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Multi-Scale Venation Pattern Analysis for Medicinal Plant Species Recognition by Arnav Sanjay Karnik, Nikhil Nair, Yashas Sagili, P. B. Pb

“…Our method extracts and analyzes venation patterns at multiple spatial scales, capturing both global and fine-grained structural details to improve classification performance. …”
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DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation by Xiyu Qi, Yidan Zhang, Lei Wang, Yifan Wu, Yi Xin, Zhan Chen, Yunping Ge

“…To exploit multiscale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. …”
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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…This limitation results in insufficient collaborative optimization between local details and macroscopic structures during denoising, further reducing the noise suppression accuracy. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. …”
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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…MultiV_Nm extracts the features of Nm sites from multiple dimensions, including sequence features, chemical characteristics, and secondary structure features. By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Multi-Scale Spatial Perception Attention Network for Few-Shot Hyperspectral Image Classification by Yang Li, Jian Luo, Haoyu Long, Qianqian Jin

“…In the encoder, the spatial contraction perception Transformer (SCPFormer) is first proposed to improve the model’s capacity for perceiving global-local joint features. Next, the multi-scale spatial attention (MSSA) module is proposed to capture spatial information at different convolution kernel scales and cascade them to form a more comprehensive representation structure. …”
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