2 citations · 2 across the 2 of their papers we have counts for
6 papers
Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer
Zeya Wang, Veera Baladandayuthapan, Ahmed O. Kaseb +4
Graphical models are commonly used to discover associations within gene or protein networks for complex diseases such as cancer. Most existing methods estimate a single graph for a…
SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
Jieli Zhou, Baoyu Jing, Zeya Wang
Due to the shortage of COVID-19 viral testing kits and the long waiting time, radiology imaging is used to complement the screening process and triage patients into different risk…
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Baoyu Jing, Zeya Wang, Eric Xing
Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologist…
Adversarial Domain Adaptation Being Aware of Class Relationships
Zeya Wang, Baoyu Jing, Yang Ni +3
Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain ada…
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (…
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be a…