Relevant bibliographies by topics / YOLO-based attention network segmentation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'YOLO-based attention network segmentation'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "YOLO-based attention network segmentation"

1

Huang, Zhihao, Jiajun Wu, Lumei Su, Yitao Xie, Tianyou Li, and Xinyu Huang. "SP-YOLO-Lite: A Lightweight Violation Detection Algorithm Based on SP Attention Mechanism." Electronics 12, no. 14 (2023): 3176. http://dx.doi.org/10.3390/electronics12143176.

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In the operation site of power grid construction, it is crucial to comprehensively and efficiently detect violations of regulations for the personal safety of the workers with a safety monitoring system based on object detection technology. However, common general-purpose object detection algorithms are difficult to deploy on low-computational-power embedded platforms situated at the edge due to their high model complexity. These algorithms suffer from drawbacks such as low operational efficiency, slow detection speed, and high energy consumption. To address this issue, a lightweight violation detection algorithm based on the SP (Segmentation-and-Product) attention mechanism, named SP-YOLO-Lite, is proposed to improve the YOLOv5s detection algorithm and achieve low-cost deployment and efficient operation of object detection algorithms on low-computational-power monitoring platforms. First, to address the issue of excessive complexity in backbone networks built with conventional convolutional modules, a Lightweight Convolutional Block was employed to construct the backbone network, significantly reducing computational and parameter costs while maintaining high detection model accuracy. Second, in response to the problem of existing attention mechanisms overlooking spatial local information, we introduced an image segmentation operation and proposed a novel attention mechanism called Segmentation-and-Product (SP) attention. It enables the model to effectively capture local informative features of the image, thereby enhancing model accuracy. Furthermore, a Neck network that is both lightweight and feature-rich is proposed by introducing Depthwise Separable Convolution and Segmentation-and-Product attention module to Path Aggregation Network, thus addressing the issue of high computation and parameter volume in the Neck network of YOLOv5s. Experimental results show that compared with the baseline network YOLOv5s, the proposed SP-YOLO-Lite model reduces the computation and parameter volume by approximately 70%, achieving similar detection accuracy on both the VOC dataset and our self-built SMPC dataset.
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Jiang, Yifan, Ziyin Wu, Fanlin Yang, et al. "YOLO-SG: Seafloor Topography Unit Recognition and Segmentation Algorithm Based on Lightweight Upsampling Operator and Attention Mechanisms." Journal of Marine Science and Engineering 13, no. 3 (2025): 583. https://doi.org/10.3390/jmse13030583.

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The recognition and segmentation of seafloor topography play a crucial role in marine science research and engineering applications. However, traditional methods for seafloor topography recognition and segmentation face several issues, such as poor capability in analyzing complex terrains and limited generalization ability. To address these challenges, this study introduces the SG-MKD dataset (Submarine Geomorphology Dataset—Seamounts, Sea Knolls, Submarine Depressions) and proposes YOLO-SG (You Only Look Once—Submarine Geomorphology), an algorithm for seafloor topographic unit recognition and segmentation that leverages a lightweight upsampling operator and attention mechanisms. The SG-MKD dataset provides instance segmentation annotations for three types of seafloor topographic units—seamounts, sea knolls, and submarine depressions—across a total of 419 images. YOLO-SG is an optimized version of the YOLOv8l-Segment model, incorporating a convolutional block attention module in the backbone network to enhance feature extraction. Additionally, it integrates a lightweight, general upsampling operator to create a new feature fusion network, thereby improving the model’s ability to fuse and represent features. Experimental results demonstrate that YOLO-SG significantly outperforms the original YOLOv8l-Segment, with a 14.7% increase in mean average precision. Furthermore, inference experiments conducted across various research areas highlight the model’s strong generalization capability.
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Guo, Jun, Tiancheng Li, and Baigang Du. "Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection." Applied Sciences 13, no. 16 (2023): 9109. http://dx.doi.org/10.3390/app13169109.

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Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer-CSP) networks to enrich the network’s ability by capturing information across different depths of network feature maps. Secondly, an insulator defect segmentation head network is presented to handle the segmentation of defect areas such as insulator losses and flashovers. It facilitates instance-level mask prediction for each insulator object, significantly reducing the influence of intricate backgrounds. Finally, comparative experiment results show that the positioning accuracy and defect segmentation accuracy of the proposed both surpass that of other popular models. It can be concluded that the proposed model not only satisfies the requirements for balance between accuracy and speed in power facility inspection, but also provides fresh perspectives for research in other defect detection domains.
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Xie, Yufei, and Liping Chen. "CBLN-YOLO: An Improved YOLO11n-Seg Network for Cotton Topping in Fields." Agronomy 15, no. 4 (2025): 996. https://doi.org/10.3390/agronomy15040996.

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The positioning of the top bud by the topping machine in the cotton topping operation depends on the recognition algorithm. The detection results of the traditional target detection algorithm contain a lot of useless information, which is not conducive to the positioning of the top bud. In order to obtain a more efficient recognition algorithm, we propose a top bud segmentation algorithm CBLN-YOLO based on the YOLO11n-seg model. Firstly, the standard convolution and multihead self-attention (MHSA) mechanisms in YOLO11n-seg are replaced by linear deformable convolution (LDConv) and coordinate attention (CA) mechanisms to reduce the parameter growth rate of the original model and better mine detailed features of the top buds. In the neck, the feature pyramid network (FPN) is reconstructed using an enhanced interlayer feature correlation (EFC) module, and regression loss is calculated using the Inner CIoU loss function. When tested on a self-built dataset, the mAP@0.5 values of CBLN-YOLO for detection and segmentation are 98.3% and 95.8%, respectively, which are higher than traditional segmentation models. At the same time, CBLN-YOLO also shows strong robustness under different weather and time periods, and its recognition speed reaches 135 frames per second, which provides strong support for cotton top bud positioning in the field environment.
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Xiong, Mengying, Aiping Wu, Yue Yang, and Qingqing Fu. "Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT." Sensors 25, no. 12 (2025): 3645. https://doi.org/10.3390/s25123645.

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Aiming at the problems of inaccurate segmentation and low detection efficiency caused by irregular tumor shape and large size differences in brain MRI images, this study proposes a brain tumor segmentation algorithm, YOLO-BT, based on YOLOv11. YOLO-BT uses UNetV2 as the backbone network to enhance the feature extraction ability of key regions through the attention mechanism. The BiFPN structure is introduced into the neck network to replace the traditional feature splicing method, realize the two-way fusion of cross-scale features, improve detection accuracy, and reduce the amount of calculations required. The D-LKA mechanism is introduced into the C3k2 structure, and the large convolution kernel is used to process complex image information to enhance the model’s ability to characterize different scales and irregular tumors. In this study, multiple sets of experiments were performed on the Figshare Brain Tumor dataset to test the performance of YOLO-BT. The data results show that YOLO-BT improves Precision by 2.7%, Recall, mAP50 by 0.9%, and mAP50-95 by 0.3% in the candidate box-based evaluation compared to YOLOv11. In mask-based evaluations, Precision improved by 2.5%, Recall by 2.8%, mAP50 by 1.1%, and mAP50-95 by 0.5%. At the same time, the mIOU increased by 6.1%, and the Dice coefficient increased by 3.6%. It can be seen that the YOLO-BT algorithm is suitable for brain tumor detection and segmentation.
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Yu, Caili, Yanheng Mai, Caijuan Yang, Jiaqi Zheng, Yongxin Liu, and Chaoran Yu. "IA-YOLO: A Vatica Segmentation Model Based on an Inverted Attention Block for Drone Cameras." Agriculture 14, no. 12 (2024): 2252. https://doi.org/10.3390/agriculture14122252.

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The growing use of drones in precision agriculture highlights the needs for enhanced operational efficiency, especially in the scope of detection tasks, even in segmentation. Although the ability of computer vision based on deep learning has made remarkable progress in the past ten years, the segmentation of images captured by Unmanned Aerial Vehicle (UAV) cameras, an exact detection task, still faces a conflict between high precision and low inference latency. Due to such a dilemma, we propose IA-YOLO (Inverted Attention You Only Look Once), an efficient model based on IA-Block (Inverted Attention Block) with the aim of providing constructive strategies for real-time detection tasks using UAV cameras. The working details of this paper are outlined as follows: (1) We construct a component named IA-Block, which is integrated into the YOLOv8-seg structure as IA-YOLO. It specializes in pixel-level classification of UAV camera images, facilitating the creation of exact maps to guide agricultural strategies. (2) In experiments on the Vatica dataset, compared with any other lightweight segmentation model, IA-YOLO achieves at least a 3.3% increase in mAP (mean Average Precision). Further validation on diverse species datasets confirms its robust generalization. (3) Without overloading the complex attention mechanism and deeper and deeper network, a stem that incorporates efficient feature extraction components, IA-Block, still possess credible modeling capabilities.
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Hua, Yue, Rui Chen, and Hang Qin. "YOLO-DentSeg: A Lightweight Real-Time Model for Accurate Detection and Segmentation of Oral Diseases in Panoramic Radiographs." Electronics 14, no. 4 (2025): 805. https://doi.org/10.3390/electronics14040805.

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Panoramic radiography is vital in dentistry, where accurate detection and segmentation of diseased regions aid clinicians in fast, precise diagnosis. However, the current methods struggle with accuracy, speed, feature extraction, and suitability for low-resource devices. To overcome these challenges, this research introduces a unique YOLO-DentSeg model, a lightweight architecture designed for real-time detection and segmentation of oral dental diseases, which is based on an enhanced version of the YOLOv8n-seg framework. First, the C2f(Channel to Feature Map)-Faster structure is introduced in the backbone network, achieving a lightweight design while improving the model accuracy. Next, the BiFPN(Bidirectional Feature Pyramid Network) structure is employed to enhance its multi-scale feature extraction capabilities. Then, the EMCA(Enhanced Efficient Multi-Channel Attention) attention mechanism is introduced to improve the model’s focus on key disease features. Finally, the Powerful-IOU(Intersection over Union) loss function is used to optimize the detection box localization accuracy. Experiments show that YOLO-DentSeg achieves a detection precision (mAP50(Box)) of 87%, segmentation precision (mAP50(Seg)) of 85.5%, and a speed of 90.3 FPS. Compared to YOLOv8n-seg, it achieves superior precise and faster inference times while decreasing the model size, computational load, and parameter count by 44.9%, 17.5%, and 44.5%, respectively. YOLO-DentSeg enables fast, accurate disease detection and segmentation, making it practical for devices with limited computing power and ideal for real-world dental applications.
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Rathinam, Vinoth, Sasireka Rajendran, and Valarmathi Krishnasamy. "A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1670–85. https://doi.org/10.11591/ijece.v15i2.pp1670-1685.

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A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
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Cao, Lianjun, Xinyu Zheng, and Luming Fang. "The Semantic Segmentation of Standing Tree Images Based on the Yolo V7 Deep Learning Algorithm." Electronics 12, no. 4 (2023): 929. http://dx.doi.org/10.3390/electronics12040929.

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The existence of humans and the preservation of the natural ecological equilibrium depend greatly on trees. The semantic segmentation of trees is very important. It is crucial to learn how to properly and automatically extract a tree’s elements from photographic images. Problems with traditional tree image segmentation include low accuracy, a sluggish learning rate, and a large amount of manual intervention. This research suggests the use of a well-known network segmentation technique based on deep learning called Yolo v7 to successfully accomplish the accurate segmentation of tree images. Due to class imbalance in the dataset, we use the weighted loss function and apply various types of weights to each class to enhance the segmentation of the trees. Additionally, we use an attention method to efficiently gather feature data while reducing the production of irrelevant feature data. According to the experimental findings, the revised model algorithm’s evaluation index outperforms other widely used semantic segmentation techniques. In addition, the detection speed of the Yolo v7 model is much faster than other algorithms and performs well in tree segmentation in a variety of environments, demonstrating the effectiveness of this method in improving the segmentation performance of the model for trees in complex environments and providing a more effective solution to the tree segmentation issue.
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Rathinam, Vinoth, Sasireka Rajendran, and Valarmathi Krishnasamy. "A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1670. https://doi.org/10.11591/ijece.v15i2.pp1670-1685.

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A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
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Dissertations / Theses on the topic "YOLO-based attention network segmentation"

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Ahmed, Mohamed. "Medical Image Segmentation using Attention-Based Deep Neural Networks." Thesis, KTH, Medicinsk avbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284224.

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During the last few years, segmentation architectures based on deep learning achieved promising results. On the other hand, attention networks have been invented years back and used in different tasks but rarely used in medical applications. This thesis investigated four main attention mechanisms; Squeeze and Excitation, Dual Attention Network, Pyramid Attention Network, and Attention UNet to be used in medical image segmentation. Also, different hybrid architectures proposed by the author were tested. Methods were tested on a kidney tumor dataset and against UNet architecture as a baseline. One version of Squeeze and Excitation attention outperformed the baseline. Original Dual Attention Network and Pyramid Attention Network showed very poor performance, especially for the tumor class. Attention UNet architecture achieved close results to the baseline but not better. Two more hybrid architectures achieved better results than the baseline. The first is a modified version of Squeeze and Excitation attention. The second is a combination between Dual Attention Networks and UNet architecture. Proposed architectures outperformed the baseline by up to 3% in tumor Dice coefficient. The thesis also shows the difference between 2D architectures and their 3D counterparts. 3D architectures achieved more than 10% higher tumor Dice coefficient than 2D architectures.
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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way.
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Book chapters on the topic "YOLO-based attention network segmentation"

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Tong, Guanjie, Haijun Lei, Limin Huang, et al. "STAU-Net: A Spatial Structure Attention Network for 3D Coronary Artery Segmentation." In Clinical Image-Based Procedures. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-23179-7_5.

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Li, Caizi, Qianqian Tong, Xiangyun Liao, et al. "Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation." In Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12029-0_28.

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Ding, Xinnan, Ying Tian, Chenhui Wang, Yilong Li, Haodong Yang, and Kejun Wang. "Attention-Based Network for Semantic Image Segmentation via Adversarial Learning." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60636-7_9.

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He, Fuyun, Yao Zhang, Yan Wei, Youwei Qian, Cong Hu, and Xiaohu Tang. "Brain Tumor Image Segmentation Network Based on Dual Attention Mechanism." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4761-4_11.

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Wang, Huan, Guotai Wang, Ze Sheng, and Shaoting Zhang. "Automated Segmentation of Skin Lesion Based on Pyramid Attention Network." In Machine Learning in Medical Imaging. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_50.

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Zhang, Sheng, Yang Nan, Yingying Fang, et al. "Fuzzy Attention-Based Border Rendering Network for Lung Organ Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72114-4_29.

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An, Peng, Yurou Xu, and Panpan Wu. "Attention Mechanism-Based Deep Supervision Network for Abdominal Multi-organ Segmentation." In Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58776-4_25.

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Roy, Ayush, Shivakumara Palaiahnakote, Umapada Pal, and Sukalpa Chanda. "A New Attention Based UNet and Gated Edge Attention Network for Retinal Vessel Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78104-9_16.

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Xu, Xiaowei, Wangyuan Zhao, and Jun Zhao. "Brain Tumor Segmentation Using Attention-Based Network in 3D MRI Images." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46643-5_1.

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Binh, Nguyen Thanh, and Tuyet Vo Thi Hong. "Improving the Polyp Image Segmentation Based on Parallel Reverse Attention Network." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-96-0434-0_6.

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Conference papers on the topic "YOLO-based attention network segmentation"

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Mu, Tongtong, Kangjian He, and Dan Xu. "Deformable coordinate kernel attention-based network for medical image segmentation." In Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), edited by Liang Xiao. SPIE, 2025. https://doi.org/10.1117/12.3057809.

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Wang, Rui, and Minghui Yao. "Overlapping chromosome segmentation model based on dual attention U-Net network." In Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), edited by Lvqing Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3038742.

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Chen, Hongyan, Donghui Yin, Zhen Qin, and Qiong Wu. "Warship Image Segmentation Network based on Void Convolution and Attention Mechanism." In 2024 9th International Conference on Signal and Image Processing (ICSIP). IEEE, 2024. http://dx.doi.org/10.1109/icsip61881.2024.10671468.

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liu, jiaqi, and Jiaqing Mo. "CAU-Net: U-shaped medical image segmentation network based on compound attention." In 2024 International Conference on Computer Vision and Image Processing, edited by Xin Xu and Zhenghao Shi. SPIE, 2025. https://doi.org/10.1117/12.3057922.

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Liu, Chaoyong, Zheng Zong, Zhike Zhang, et al. "An Choroidal Segmentation Network Based on Attention Mechanisms and Self-Calibrated Convolutions." In 2025 5th International Conference on Advances in Electrical, Electronics and Computing Technology (EECT). IEEE, 2025. https://doi.org/10.1109/eect64505.2025.10966990.

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Xu, Hongli, and Jue Wu. "LA-TransUNet: Intracranial Hemorrhage CT Image Segmentation Network Based on Attention Mechanism." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906179.

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Liu, Shuting, Aina Wang, and Pan Qin. "A Channel Attention Enhancement Network Based on TransUNet for Tiny Intracerebral Hemorrhage Segmentation." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661661.

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Noroozi, M., H. Arabi, and H. Zaidi. "Attention-Based Network for Cardiac Systolic Abnormality Segmentation in Catheterization X-ray Images." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10655946.

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Pal, Debojyoti, Tanushree Meena, Dwarikanath Mahapaatra, and Sudipta Roy. "SAC UW-Net: A self-attention-based network for multimodal medical image segmentation." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635611.

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Li, Han, Chaoguang Men, and Yongmei Liu. "Semantic segmentation of airborne LiDAR point cloud based on global geometric attention network." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00061.

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