MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…To address the issue of lacking texture and structural information in the target images, we employ an integrated differential convolution (IDConv) module to extract richer image features during both the encoding and decoding stages. …”
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LCCDMamba: Visual State Space Model for Land Cover Change Detection of VHR Remote Sensing Images by Junqing Huang, Xiaochen Yuan, Chan-Tong Lam, Yapeng Wang, Min Xia

“…The proposed MISF comprises multi-scale feature aggregation (MSFA), which utilizes strip convolution to aggregate multiscale local change information of bitemporal land cover features, and residual with SS2D (RSS) which employs residual structure with SS2D to capture global feature differences of bitemporal land cover features. …”
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Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

“…Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…These conditions are significant global health concerns, affecting millions and leading to vision loss if not diagnosed promptly. …”
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SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation by Yang Zhou Ling Ou, Joon Huang Chuah, Hua Nong Ting, Shier Nee Saw, Jun Zhao

“…Specifically, a parallel encoder architecture employs a Convolutional (Conv) block with a residual structure for local feature extraction alongside a hierarchical Swin Transformer with Shifted-Window Multi-head Self-Attention (SW-MSA) for global context modeling, thereby achieving comprehensive feature capture with minimal additional parameter overhead. …”
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Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes by Jalil Jalili, Evan Walker, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Massimo A. Fazio, Christopher A. Girkin, Carlos Gustavo De Moraes, Jeffrey M. Liebmann, Robert N. Weinreb, Linda M. Zangwill, Mark Christopher

“…We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R² = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R² from 0.94–0.98). …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…The SggNet adopts a classical encoder-decoder structure with MobileNet-V2 as the backbone, ensuring optimal parameter utilization. …”
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Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration by Dongryul Oh, Sujin Kang, Heejin Kim, Dongsuk Oh

“…Graphs inherently encode intricate structural dependencies, requiring models to effectively capture both local and global relationships. …”
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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…First, the depthwise separable convolution in the Xception structure is used to optimize the extraction of fault features. …”
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DSGAU: Dual-Scale Graph Attention U-Nets for Hyperspectral Image Classification With Limited Samples by Hongzhuang Ji, Leying Song, Zhaohui Xue, Hongjun Su

“…This enables the simultaneous learning of local spectral features and global contextual patterns within HSI data. However, the convolutional operations in traditional GCNs require the inclusion of all data points during graph construction, leading to significant computational overhead, particularly for large-scale datasets. …”
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Dynamic Graph Attention Network for Skeleton-Based Action Recognition by Zhenhua Li, Fanjia Li, Gang Hua

“…To address these challenges, we propose a Dynamic Graph Attention Network (DGAN) that dynamically integrates local structural features and global spatiotemporal dependencies. …”
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Multi-granularity representation learning with vision Mamba for infrared small target detection by Yongji Li, Luping Wang, Shichao Chen

“…Specifically, we tailor a nested structure with cross-fertilization of global and local information. …”
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Anterior Cruciate Ligament (ACL) Tear Detection Using Hybrid CNN Transformer by Suthir Sriram, Deependra K. Singh, D. V. Sairam, Nivethitha Vijayaraj, Thangavel Murugan

“…Firstly, MambaConvT utilizes multi-core convolutional networks to achieve higher extraction capability of the ACL tear specific local features from structural MR (Magnetic Resonance) images. …”
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GKCAE: A graph-attention-based encoder for fine-grained semantic segmentation of high-voltage transmission corridors scenario LiDAR data by Su Zhang, Haibo Liu, Jingguo Rong, Yaping Zhang

“…GKCAE first captures local geometric features using Kernel Point Convolution, and then models inter-class spatial relationships through Graph Edge-Conditioned Convolution to incorporate global contextual information. …”
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Triangular Mesh Surface Subdivision Based on Graph Neural Network by Guojun Chen, Rongji Wang

“…The tensor voting strategy was used to replace the half-flap spatial transformation method of neural subdivision to ensure the translation, rotation, and scaling invariance of the algorithm. Dynamic graph convolution was introduced to learn the global features of the mesh in the way of stacking, so as to improve the subdivision effect of the network on the extreme input mesh. …”
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Enhanced ResNet50 for Diabetic Retinopathy Classification: External Attention and Modified Residual Branch by Menglong Feng, Yixuan Cai, Shen Yan

“…In this study, we propose an improved ResNet50 model, which replaces the 3 × 3 convolution in the residual structure by introducing an external attention mechanism, which improves the model’s awareness of global information and allows the model to grasp the characteristics of the input data more thoroughly. …”
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MRP-YOLO: An Improved YOLOv8 Algorithm for Steel Surface Defects by Shuxian Zhu, Yajie Zhou

“…It is further proposed that the RepHead detection head approximates the multi-branch structure of the original training by a single convolution operation. …”
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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…With the help of atrous convolutions, local and global dependencies within the EEGs can be effectively modeled in this way. …”
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Occlusion Removal in Light-Field Images Using CSPDarknet53 and Bidirectional Feature Pyramid Network: A Multi-Scale Fusion-Based Approach by Mostafa Farouk Senussi, Hyun-Soo Kang

“…To preserve efficiency without sacrificing the quality of the extracted feature, our model uses separable convolutional blocks. A simple refinement module based on half-instance initialization blocks is integrated to explore the local details and global structures. …”
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