Global Bank: A Guided Pathway of Encoding and Decoding for Pathological Image Analysis The encoderdecoder architecture of convolutional neural networks (CNNs) is widely used in computer vision tasks and various analyses of medical images. However, extracting semantic features from regions of interest (RoIs) in pathological images remains a challenging task because RoIs of different morphologies and scales are embedded in a blurred background. Additionally, it is well known that the classic encoderdecoder architecture is vulnerable to interference from a blurred background and is thus not entirely suitable for precise analysis of pathological images. In this paper, we propose a pathway named global bank (GLB) to guide the encoder and decoder to focus more on the RoIs by providing the decoder with additional effective features of the RoIs. We extend the UNet and feature pyramid network (FPN) with GLB and evaluate the resulting models on gland segmentation and cancer embolus detection tasks, respectively. Extensive experiments demonstrate that our proposal can significantly improve the performance of the encoderdecoder architecture. The UNet with GLB achieves the best semantic segmentation performance on the 2015 MICCAI Gland Challenge dataset. Additionally, the FPN with GLB achieves improvements of 2% in average precision and 3.4% in recall on the embolus detection task.

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.