A Survey on the New Generation of Deep Learning in Image Processing <s> 7) DESCRIPTION OF OTHER MODELS IN CNN SERIES <s> There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net . <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 7) DESCRIPTION OF OTHER MODELS IN CNN SERIES <s> 1st International Workshops on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016 and 1st International Workshops on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Greece, 17-21 October, 2016 <s> BIB002
U-Net is an improvement based on FCN architecture. Its network architecture consists of two parts: contraction path and extension path BIB001 . The shrinking path is mainly used to capture the context information about the picture, while the expanding path used to precisely locate the parts that need to the segment of the picture. Compared with FCN, the highpixel feature extracted by U-Net in contraction path will be combined with the new feature map in the up-sampling process to maximize the retention of some important feature information in the previous down-sampling process. Besides, there is no full connected layer in the whole network, which can minimize the number of training parameters. And the U-shaped structure can better retain the information about the picture. As we all know, classical deep learning needs abundant samples and expensive computing resources; however, U-Net can be used for small sample learning. In particular, this network is suitable for medical-related image segmentation tasks. Compared with ResNet, DenseNet's innovation lies in the outputs of each layer are connected with all successor layers in a dense block, the feature maps learned by this layer are also passed directly to all the layers behind it as input  . Another highlight of DenseNet is efficient for feature reuse, which dramatically reduces network parameters. It is worth pointing out that dense connection directly connects input and loss in each layer, thus alleviating the phenomenon of gradient vanishing. Finally, This new model shows stateof-the-art accuracy with a reasonable number of network parameters for the object recognition tasks. FractalNet is an advanced and alternative architecture of the ResNet model, which is another efficient for designing large models with nominal depth BIB002 . Unlike ResNet, FractalNet demonstrates that path length is essential for training ultra-deep neural networks, and residual learning is not necessary for ultra-deep networks. Unlike ResNet, the performance of FractalNet trained by dropout and drop-path tricks often surpasses that of ResNet.