Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis by Jianhua Sun, Ye Cao, Ying Zhou, Baoqiao Qi

“…The research aims to provide an efficient and reliable tool for clinicians to aid in the diagnosis of knee joint disorders, particularly focusing on Anterior Cruciate Ligament (ACL) tears.MethodsKneeXNet leverages the power of graph convolutional networks (GCNs) to capture the intricate spatial dependencies and hierarchical features in knee MRI scans. …”
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Influence of slaughter age on meat quality, metabolite and volatile organic compound changes in Lueyang black-bone chickens by Jie Gao, Ning Chen, Jia Cheng, Zhengnan Yuan, Wenxian Zeng, Hongzhao Lu, Tao Zhang, Ling Wang

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Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection by Mihaly Csontho, Andras Rovid

“…This paper presents Pillar-X, a 3D object recognition framework designed to generate and use self-learned image features. Unlike approaches that rely on pre-trained networks, Pillar-X efficiently learns and fuses features from an image directly within an end-to-end pipeline. …”
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Neural network pruning based on channel attention mechanism by Jianqiang Hu, Yang Liu, Keshou Wu

“…However, most of the existing methods ignore the differences in the contributions of the output feature maps. In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. …”
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Spatial-spectral collaborative attention network for hyperspectral unmixing by Xiaojie Chen, Fanlei Meng, Ye Mo, Haixin Sun

“…In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). …”
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Virtual sample based techniques using deep features for SSPP face recognition in unconstrained environment. by Muhammad Tariq Siddique, Ibrahim Venkat, Humera Farooq, Sharul Tajuddin, S H Shah Newaz

“…A local region-based technique was proposed to deal with occlusion by creating virtual samples. A deep neural network-based model, FaceNet, was used to extract the features and a support vector machine was used for classification. …”
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Lightweight Blockchain for Data Integrity and Traceability in IoT Networks by Laura Garcia, Carlos Cancimance, Rafael Asorey-Cacheda, Claudia-Liliana Zuniga-Canon, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro

“…In this paper, we propose a lightweight blockchain for data integrity and traceability in IoT networks that adapts the Distributed Ledger Technology (DLT) feature of blockchain to the LoRaWAN wireless communication protocol. …”
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A Novel Method for Community Detection in Bipartite Networks by Ali Khosrozadeh, Ali Movaghar, Mohammad Mehdi Gilanian Sadeghi, Hamidreza Mahyar

“…The community structure is a major feature of bipartite networks, which serve as a typical model for empirical networks consisting of two kinds of nodes. …”
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Sensitivity of Spiking Neural Networks Due to Input Perturbation by Haoran Zhu, Xiaoqin Zeng, Yang Zou, Jinfeng Zhou

“…However, existing methods fall short in evaluating the sensitivity of SNNs featuring biologically plausible leaky integrate-and-fire (LIF) neurons due to the intricate neuronal dynamics during the feedforward process. …”
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Dual-Path Adversarial Denoising Network Based on UNet by Jinchi Yu, Yu Zhou, Mingchen Sun, Dadong Wang

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Binarized Neural Networks for Resource-Efficient Spike Sorting by Luca M. Meyer, Majid Zamani, Andreas Demosthenous

“…The novelty of this work resides in the developed network architecture. In comparison to previous research, this work presents a deep binarized neural network featuring two hidden layers, each containing 256 units to effectively capture the spike characteristics of complex neural data. …”
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Att-BEVFusion: An Object Detection Algorithm for Camera and LiDAR Fusion Under BEV Features by Peicheng Shi, Mengru Zhou, Xinlong Dong, Aixi Yang

“…Then, a channel attention mechanism is introduced to design a BEV feature fusion network to realize the fusion of camera BEV feature space and LiDAR BEV feature space. …”
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DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation by Yongchao Duan, Rui Yang, Ming Zhao, Mingrui Qi, Sheng-Lung Peng

“…However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. …”
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Pangenome analysis of transposable element insertion polymorphisms reveals features underlying cold tolerance in rice by Yongqing Qian, Zuwen Zhou, Tianmin Ouyang, Dongao Li, Ru Li, Ping Gan, Renfei Qiao, Yingying Tan, Mingchao Qian, Liezhao Liu, Jiana Li, Kun Lu, Jijing Luo, Ling-Ling Chen, Jia-Ming Song

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Marine object detection in forward-looking sonar images via semantic-spatial feature enhancement by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Nan Xu

“…Specifically, we introduce the competitive coordinate attention mechanism (CCAM) and the spatial group enhance attention mechanism (SGEAM), both integrated into the backbone network to effectively capture semantic and spatial features within sonar images, while feature fusion is employed to suppress complex marine background noise. …”
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Multi-CNN Deep Feature Fusion and Stacking Ensemble Classifier for Breast Ultrasound Lesion Classification by Kemal PANÇ, Sümeyye SEKMEN

“…Conclusion: The proposed approach, combining multi-convolutional neural network deep feature fusion, optimized feature selection, and ensemble stacking, shows significant potential for automated breast ultrasound classification, especially for benign and normal cases. …”
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AFDR-Det: Adaptive Feature Dual-Refinement Oriented Detector for Remote Sensing Object Detection by Jie Yang, Li Zhou, Yongfeng Ju

“…Additionally, most head networks use two branch networks to implement object localization and classification tasks separately, resulting in a lack of information interaction between the classification and localization tasks, leading to spatial feature misalignment issues. …”
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Vision Transformer Embedded Feature Fusion Model with Pre-Trained Transformers for Keratoconus Disease Classification by Md Fatin Ishrak, Md Maruf Rahman, Md Imran Kabir Joy, Anna Tamuly, Salma Akter, Dewan M. Tanim, Shahajada Jawar, Nayeem Ahmed, Md Sadekur Rahman

“…The primary objective of this research is to develop a feature fusion hybrid deep learning framework that integrates pretrained Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for the automated classification of keratoconus into three distinct categories: Keratoconus, Normal, and Suspect. …”
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Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks by Mohamed Fathallah, Sherif Eletriby, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farok

“…Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. …”
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Graph neural network-tracker: a graph neural network-based multi-sensor fusion framework for robust unmanned aerial vehicle tracking by Karim Dabbabi, Tijeni Delleji

“…In this study, we propose graph neural network-tracker (GNN-tracker), a novel GNN-based UAV tracking framework that effectively integrates graph-based spatial-temporal modelling, Transformer-based feature extraction, and multi-sensor fusion to enhance tracking robustness and accuracy. …”
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