基于3D ResUnet网络的肺结节分割 - 1357 - 节,但是区域生长的起始种子点需要手工标注选取, 基于传统方法的局限性 ,目前主要采用的方法是将传统的方法和机器学习的算法结合起来 ,或者采用 基于深度学习的方法,如 R-CNN 系列[8],U-Net[9]等, 严忱君[10]采用 U-Net 网络提取候选区域实现早期肺 结节检测,并...
将提取的编码解码的中间部分使用pooling进行信息抽取之后把这部分信息（从3D）抽取的信息，通过GAN生成对应...
To address the above challenges, this paper presents ReSU-Net, A novel multi-class medical image segmentation model that synergistically combines the strengths of Transformer, State Space Model (SSM), and residual connection. The core component of ReSU-Net is the Residual State Space Module (R...
resunet模型结构ResUNet(Residual UNet)是一种用于图像分割的深度学习模型,它结合了UNet的结构优势和残差学习的思想,以提高模型的性能和稳定性。UNet最初是由Olaf Ronneberger等人于2015年提出,用于生物医学图像分割。它的特点是有一个对称的“U”形结构,包括一个下采样(编码器)路径和一个上采样(解码器)路径,以及...
本文将贝叶斯近似推断理论应用于分割网络中,进一步提高了息肉病灶区域的边缘分割效果.3,为了进一步验证改进方法的有效性,本文将适用于2D息肉图像的PolypSeg-MRU分割框架拓展为3D医学图像分割框架(命名为3D-Res-ResUnet),并将其应用于3D-MRI影像数据集中,实验结果表明3D-Res-ResUnet分割模型分割效果良好,平均Dice相关系数...
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In this paper, we propose a 3D self-ensemble ResUNet (srUNet) deep neural network architecture for brain tumor segmentation and machine learning-based method for overall survival prediction of patients with gliomas. UNet architecture has been using for semantic image segmentation. It also been used...
In this paper, we propose a multimodal brain tumor segmentation using a 3D ResUNet deep neural network architecture. Deep neural network has been applying in many domains, including computer vision, natural language processing, etc. It has also been used for semantic segmentation in medical imaging...
To enhance the encryption of medical images, a number of encryption approaches were suggested and created. This research proposes a hyperchaotic map as well as a deep learning (DL) based selective medical image encryption technique. Initially, the 3D_Att_ResU-Net segments...
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