S3 Net: Trained on a Small Sample Segmentation Network for Biomedical Image Analysis Fully convolutional networks (FCNs) are powerful methods to extract hierarchies of features that have achieved remarkable success in various biomedical image analysis tasks. However, the successful training of FCN requires more than hundreds of pixellevel annotated training samples, which poses a challenge for biomedical image processing tasks. In this paper, we present  S3  Net, a network that makes more efficient use of available annotated samples on biomedical image segmentation.  S3  Net is essentially a deeplysupervised encoderdecoder network where the decoder has been redesigned to efficiently restore multilevel encoded feature resolution in a single step. We have conducted extensive experiments on the 2015 MICCAI Gland Challenge dataset. Compared with other methods,  S3  Net achieves a 0.781 dicescore using 11 images for training, higher than UNet 18.6% and UNet 12.9%, which verifies the performance of  S3  Net trained on a small sample. Further, with training on 85 images,  S3  Net achieves a 0.910 dicescore by using resnet50 as the encoder, which is higher than stateoftheart semantic segmentation results by 4%.

A Generalized Densely Connected EncoderDecoder Network for epithelial and stromal regions segmentation in histopathological images Identification of epithelial and stromal regions by a computerized system is quite challenging due to their irregular shape and size in histopathological images. Nowadays, although convolutional neural networks(CNNs) have significantly push forward this field, the typical use of CNNs in histopathological images is to classify an image block into a corresponding category. It fails to enable a better adherence of class boundaries due to the tradeoff between networksu0027 localization accuracy and input context. Larger blocks require more pooling layers that reduce localization accuracy, while tiny blocks allow networks to see only a small amount of context. So in this paper, we propose a generalized densely connected encoderdecoder network to deal with this problem. The main idea behind our network is captured in dense skip connections to compensate for resolution loss induced by pooling layers. So our network can have a large amount of context as input without losing localization accuracy. Finally, we show that the proposed network can outperform UNet on Stanford Tissue Microarray Database without any further postprocessing module or pretraining. Moreover, due to smart construction of the model, our approach has much fewer parameters than currently published best entries for this dataset. This means our approach is much faster than currently published entries. Segmentation of a  512times 512  image only takes 34ms on an Nvidia Tesla P100 GPU.