Posts Tagged ‘Minimal Information Loss Attention U-Net’
Complete List of Challenge Submissions
Minimal Information Loss Attention U-Net for abdominal CT of kidney cancers segmentation
Liu, Yubai
Jia, Fucang
Qi, Shouliang
Recent work has shown that U-net is a straight-forward and successful architecture, it quickly evolved to a commonly used benchmark in medical image segmentation, Which nnU-Net had better performance We improved the nnUNet model by incorporating a new image pyramid to preserve contextual features and attention gate. In order to let different kinds of class details more easily accessible at different scales, we injected the encoder layers with an input image pyramid before each of the max-pooling layers. We proposed a new image pyramid mechanism with dilated convolution that counters the loss of information caused by max-pooling, re-introducing the original image at multiple points within the network. We evaluated this model in the 2019 Kidney Tumor Segmentation Challenge. and got the dice coefficient 0.958 of kidney and 0.847 of tumors.