86 citations · 397 across the 60 of their papers we have counts for
5 papers · 1 filter
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
Yuxing Tang, Xiaosong Wang, Adam P. Harrison +3
In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupl…
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Youbao Tang, Jinzheng Cai, Le Lu +5
Automated lesion segmentation from computed tomography (CT) is an important and challenging task in medical image analysis. While many advancements have been made, there is room fo…
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D seg…
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri +2
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations m…
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively…