5 Result(s)
within
Conference Paper
Qi Dou
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Chapter and Conference Paper
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
Accurate acquisition of fetal ultrasound (US) standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fetal ...
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Chapter and Conference Paper
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised ne...
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Chapter and Conference Paper
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
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 treatment of cardiovascular diseas...
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Chapter and Conference Paper
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
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 yet crucial task for lung cance...
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Chapter and Conference Paper
MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis
Lung cancer is the leading cause of cancer deaths worldwide. Early diagnosis of lung nodules is of great importance for therapeutic treatment and saving lives. Automated lung nodule analysis requires both accu...
