Deep learning has witnessed a huge amount of attention over the last decade.It has been widely used in the field of medical images, especialy the deep neural networks for classification, detection and segmentation task. However, when compared with natural images, medical images are very difficult to label. In most cases, the size of the data is difficult to match the natural image database.To address this problem, the traditional approach is through the use of transfer learning methods, by using ImageNet pre-trained models. But medical images are often very different from natural images, the light and dark of the images have special meanings, and the images contain internal structures of the human body that are completely absent from the natural image data set.