Implementation of biomedical segmentation for brain tumor utilizing an adapted U-net model [0.03%]
基于适配的U型网络模型的脑肿瘤医学图像分割方法
Farah F Alkhalid,Nibras Z Salih
Farah F Alkhalid
Using radio signals from a magnetic field, magnetic resonance imaging (MRI) represents a medical procedure that produces images to provide more information than typical scans. Diagnosing brain tumors from MRI is difficult because of the wid...
Research on CT image segmentation and classification of liver tumors based on attention mechanism and improved U-Net model [0.03%]
基于注意力机制和改进U-Net模型的肝脏肿瘤CT图像分割与分类研究
Guang Mei,Jinhua Yu
Guang Mei
BackgroundLiver cancer is still one of the most common causes of death from cancer globally. The accurate segmentation of liver tumors from CT images is critical for diagnosis, treatment planning, and tracking. Conventional segmentation tec...
A Mamba U-Net Model for Reconstruction of Extremely Dark RGGB Images [0.03%]
一种用于极弱光RGGB图像重建的Mamba-U-net模型的方法及系统
Yiyao Huang,Xiaobao Zhu,Fenglian Yuan et al.
Yiyao Huang et al.
Currently, most images captured by high-pixel devices such as mobile phones, camcorders, and drones are in RGGB format. However, image quality in extremely dark scenes often needs improvement. Traditional methods for processing these dark R...
Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods [0.03%]
基于低剂量CT血管造影图像的脑动脉分割的U-Net模型可行性研究及预处理方法
Seong-Hyeon Kang,Kyuseok Kim,Jina Shim et al.
Seong-Hyeon Kang et al.
Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high r...
Diffusion-CSPAM U-Net: A U-Net model integrated hybrid attention mechanism and diffusion model for segmentation of computed tomography images of brain metastases [0.03%]
一种集成混合注意力机制和扩散模型的U网模型用于脑转移瘤CT图像分割
Yiren Wang,Zhongjian Wen,Shuilan Bao et al.
Yiren Wang et al.
Background: Brain metastases are common complications in patients with cancer and significantly affect prognosis and treatment strategies. The accurate segmentation of brain metastases is crucial for effective radiation t...
MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans [0.03%]
MWG-UNet++:用于MRI扫描脑肿瘤分割的混合变压器U型网络模型
Yu Lyu,Xiaolin Tian
Yu Lyu
The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to subop...
A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging [0.03%]
一种基于新型深度学习的(3D U-Net模型)计算机断层成像自动肺结节检测工具
Abhishek Mahajan,Rajat Agarwal,Ujjwal Agarwal et al.
Abhishek Mahajan et al.
Background: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management. Objecti...
Algorithm for pixel-level concrete pavement crack segmentation based on an improved U-Net model [0.03%]
基于改进U-Net模型的路面裂缝像素级分割算法
Zixuan Zhang,Yike He,Di Hu et al.
Zixuan Zhang et al.
Cracks that occur in concrete surfaces are numerous and diverse, and different cracks will affect road safety in different degrees. Accurately identifying pavement cracks is crucial for assessing road conditions and formulating maintenance ...
Improving diagnostic precision in thyroid nodule segmentation from ultrasound images with a self-attention mechanism-based Swin U-Net model [0.03%]
基于自注意力机制的Swin U-Net模型提高超声图像甲状腺结节分割的诊断精度
Changan Yang,Muhammad Awais Ashraf,Mudassar Riaz et al.
Changan Yang et al.
Background: Accurate segmentation of thyroid nodules in ultrasound imaging remains a significant challenge in medical diagnostics, primarily due to edge blurring and substantial variability in nodule size. These challenge...
ACU-Net: Attention-based convolutional U-Net model for segmenting brain tumors in fMRI images [0.03%]
ACU-Net:一种基于注意力机制的卷积U型网络模型，用于fMRI图像中脑肿瘤的分割
Md Alamin Talukder,Md Abu Layek,Md Aslam Hossain et al.
Md Alamin Talukder et al.
Objective: Accurate segmentation of brain tumors in medical imaging is essential for diagnosis and treatment planning. Current techniques often struggle with capturing complex tumor features and are computationally demand...
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