6, we can see that the curve corresponding to UNet-Ensemble is closer to the diagonal than those of UNet and UNet-MC, which indicates better calibration. Looking at the right graph in Fig. 6, we can see that the three DST-based models, ENN-UNet, RBF-UNet, and MMEF-UNet, have ...
Then, in each layer of the encoder sub-network, we propose a newly pyramid edge enhancement module with edge-related prior knowledge to obtain the edge multi-granularity information, which enhances the accuracy of the segmentation boundary. In the BCF-UNet, edge information is peculiarly utilized ...
CNN is a multi-layer neural network containing convolution, pooling, activation and fully connected layers. Convolution layers are the core of CNNs and are used for feature extraction. The convolution operation can produce different feature maps depending on the filters used. Pooling layer performs ...
processing outputs of TB into full-size segmentation maps. TB largely follows the structure of the recent SSFormer [32] which predicts segmentation maps ofh4×w4spatial dimensions, and which achieved the current state-of-the-art performance on polyp segmentation at reduced-size. However, we update...
CODE:https://github.com/mindflow-institue/TransCeption cnn缺少长距离依赖,最近的一些工作用了transformer来解决,还有一些工作扩展了u-net的多尺度特征提取和fusion,但是都有一定的缺陷 如: unet和transformer结合的方法: 低维特征和高维特征不能在transformer中充分融合,多级的特征之间还有交互的余地。
First, adaptive fusion is applied to the features of each layer of the encoder, enabling the AFFM module to adaptively select appropriate features for fusion based on the size and characteristics of the tumor in the CT image. This ensures that the detailed information at each encoder stage is...
In computer vision, convolution and pooling operations tend to lose high-frequency information, and the contour details will also disappear with the deepening of the network, especially in image semantic segmentation. For RGB-D image semantic segmentation, all the effective information of RGB and dept...
For the outputs of Layer0, Layer1, and Layer2, feature extraction is per- formed using convolutions with dilated rates of 12, 6, and 3, respectively, and the corresponding convolution kernel sizes with dilated convolutions are 49, 25, and 13, whose original convolution kernel size is 5. ...
Pulmonary embolism (PE) is a common, life threatening cardiovascular emergency. Risk stratification is one of the core principles of acute PE management and determines the choice of diagnostic and therapeutic strategies. In routine clinical practice, cli
While FPN [24] can overcome these drawbacks to retain multi-scale contextual information by using multiple prediction layers: one for each up-sampling layer. Based on this idea, we propose a novel segmentation scheme for the infections of COVID-19. Fig. 2 illustrates the proposed network ...