4 citations · 5 across the 3 of their papers we have counts for
3 papers
eess.IV2023★ 4 cited
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos
Li Chen, Jonathan Rubin, Jiahong Ouyang +24
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is li…
eess.IV2023★ 1 cited
How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound
Menghan Yu, Sourabh Kulhare, Courosh Mehanian +5
Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthc…
eess.IV2023
Weakly Semi-Supervised Detection in Lung Ultrasound Videos
Jiahong Ouyang, Li Chen, Gary Y. Li +24
Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data. We propose a method for i…