3 citations · 4 across the 4 of their papers we have counts for
5 papers
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Shanlin Sun, Yang Liu, Narisu Bai +5
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning. Recently, deep learning based methods for OAR delineation have…
NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation
Hao Tang, Chupeng Zhang, Xiaohui Xie
Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images. Methods have been p…
Automatic Pulmonary Lobe Segmentation Using Deep Learning
Hao Tang, Chupeng Zhang, Xiaohui Xie
Pulmonary lobe segmentation is an important task for pulmonary disease related Computer Aided Diagnosis systems (CADs). Classical methods for lobe segmentation rely on successful d…
An End-to-end Framework For Integrated Pulmonary Nodule Detection and False Positive Reduction
Hao Tang, Xingwei Liu, Xiaohui Xie
Pulmonary nodule detection using low-dose Computed Tomography (CT) is often the first step in lung disease screening and diagnosis. Recently, algorithms based on deep convolutional…
Automated pulmonary nodule detection using 3D deep convolutional neural networks
Hao Tang, Daniel R. Kim, Xiaohui Xie
Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convo…