Abstract
In the image classification task, we only need to learn local features, but in the image segmentation task, we also need to learn positional information. Therefore, there is a difference between the image segmentation task and the image classification task in the features to be learned. In this study, we propose SE-U-Net++, which efficiently learns both local features and positional information by incorporating SE blocks, and a transfer learning algorithm that bridges the difference between the tasks by comparing parameters in the convolutional layer.
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