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Pyramid Scene Parsing Network
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregationExpand
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ICNet for Real-Time Semantic Segmentation on High-Resolution Images
We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion ofExpand
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DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation
The morphology of glands has been used routinely by pathologists to assess the malignancy degree of adenocarcinomas. Accurate segmentation of glands from histology images is a crucial step to obtainExpand
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H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentationExpand
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Gland segmentation in colon histology images: The glas challenge contest
&NA; Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, includingExpand
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GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometricExpand
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Semi-Parametric Image Synthesis
We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. TheExpand
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The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 andExpand
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Augmented Feedback in Semantic Segmentation Under Image Level Supervision
Training neural networks for semantic segmentation is data hungry. Meanwhile annotating a large number of pixel-level segmentation masks needs enormous human effort. In this paper, we propose aExpand
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DCAN: Deep contour‐aware networks for object instance segmentation from histology images
HIGHLIGHTSMulti‐level fully convolutional networks for effective object segmentation.A novel method to harness information of object appearance and contour simultaneously.Transfer learning toExpand
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