9 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National Institutes of HealthUS3 papers
- Ping An (China)3 papers
- National Institutes of Health Clinical CenterUS2 papers
- University of FloridaUS2 papers
- Brandeis UniversityUS1 paper
- Northwestern UniversityUS1 paper
- Nvidia (United States)US1 paper
- University of KwaZulu-NatalZA1 paper
- University of PennsylvaniaUS1 paper
- University of WaterlooCA1 paper
- Virginia TechUS1 paper
5 papers · 1 filter
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Hanqing Li, Diego Klabjan, Jean Utke
This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simp…
Learning from Multiple Datasets with Heterogeneous and Partial Labels for Universal Lesion Detection in CT
Ke Yan, Jinzheng Cai, Youjing Zheng +7
Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often eit…
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…