Abstract
Our team participate in the challenge of Task 1: Lesion Boundary Segmentation , and use a combined network, one of which is designed by ourselves named updcnn net and another is an improved VGG 16-layer net. Updcnn net uses reduced size images for training, and VGG 16-layer net utilizes large size images. Image enhancement is used to get a richer data set. We use boxes in the VGG 16-layer net network for local attention regularization to fine-tune the loss function, which can increase the number of training data, and also make the model more robust. In the test, the model is used for joint testing and achieves good results.
Abstract (translated)
我们的团队参与任务1：病变边界分割的挑战，并使用组合网络，其中一个由我们自己设计，名为updcnn net，另一个是改进的VGG 16层网络。 Updcnn net使用缩小尺寸的图像进行训练，VGG 16层网使用大尺寸图像。图像增强用于获取更丰富的数据集。我们使用VGG 16层网络中的盒子进行局部注意正规化，以微调损耗函数，这可以增加训练数据的数量，并使模型更加健壮。在测试中，该模型用于联合测试并获得良好的结果。
URL
https://arxiv.org/abs/1807.07391
