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SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Yanning Zhou, Qi Dou, Hao Chen +2
Cell nuclei detection and fine-grained classification have been fundamental yet challenging problems in histopathology image analysis. Due to the nuclei tiny size, significant inte…
Direction-aware Spatial Context Features for Shadow Detection
Xiaowei Hu, Lei Zhu, Chi-Wing Fu +2
Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper pr…
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Xiaomeng Li, Hao Chen, Xiaojuan Qi +3
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…
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
Qi Dou, Hao Chen, Yueming Jin +3
In this paper, we propose a novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging ye…
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Lequan Yu, Jie-Zhi Cheng, Qi Dou +4
Automatic and accurate whole-heart and great vessel segmentation from 3D cardiac magnetic resonance (MR) images plays an important role in the computer-assisted diagnosis and treat…
ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images
Huangjing Lin, Hao Chen, Qi Dou +3
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists. In recent years,…