Results: The quantitative evaluations of the 3D SE-DenseNet on a two-class HCC grading task were conducted based on the dataset, which included 213 samples of the dynamic enhanced MR images. The proposed 3D SE-DenseNet demonstrated an accuracy of 83%, when compared with the 72% accuracy of ...
接下来,类似于ResNet使用的4个残差块,DenseNet使用的是4个稠密块。与ResNet类似,我们可以设置每个稠密块使用多少个卷积层。这里我们设成4,从而与 :numref:sec_resnet的ResNet-18保持一致。稠密块里的卷积层通道数(即增长率)设为32,所以每个稠密块将增加128个通道。 在每个模块之间,ResNet通过...
Conclusions: Both DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multi-modal MR images. The multi-path DenseNet achieved more accurate tumor segmentation than the single-path DenseNet. Here presented the 3D multi-path DenseNet that demonstrated an ...
3D DenseNetSENetHepatocellular carcinomaGradingMRIBackground: Clinical histological grading of hepatocellular carcinoma (HCC) differentiation is of great significance in clinical diagnoses, treatments, and prognoses. However, it is challenging for radiologists to evaluate HCC gradings from medical images.Zhou,...