activity
20192026
most citedContrast Phase Classification with a Generative Adversarial Network

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis

Chaoqin Huang, Zi Zeng, Aofan Jiang +6

Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities i…

eess.IV2020

Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Yuchen Xu, Olivia Tang, Yucheng Tang +9

Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…

cs.CV2020★ 1 cited

Outlier Guided Optimization of Abdominal Segmentation

Yuchen Xu, Olivia Tang, Yucheng Tang +9

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…

eess.IV2019★ 3 cited

Contrast Phase Classification with a Generative Adversarial Network

Yucheng Tang, Ho Hin Lee, Yuchen Xu +10

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…

eess.IV2019

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Ho Hin Lee, Yucheng Tang, Olivia Tang +9

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…