56 citations · 125 across the 25 of their papers we have counts for
7 papers · 1 filter
Decentralized Unsupervised Learning of Visual Representations
Yawen Wu, Zhepeng Wang, Dewen Zeng +3
Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative lear…
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation
Xinrong Hu, Dewen Zeng, Xiaowei Xu +1
The success of deep learning methods in medical image segmentation tasks heavily depends on a large amount of labeled data to supervise the training. On the other hand, the annotat…
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays
Dewen Zeng, John N. Kheir, Peng Zeng +1
Contrastive learning has been proved to be a promising technique for image-level representation learning from unlabeled data. Many existing works have demonstrated improved results…
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
Dewen Zeng, Yukun Ding, Haiyun Yuan +5
Automatic myocardial segmentation of contrast echocardiography has shown great potential in the quantification of myocardial perfusion parameters. Segmentation quality control is a…
Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography
Dewen Zeng, Mingqi Li, Yukun Ding +7
Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many app…
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast
Yawen Wu, Zhepeng Wang, Dewen Zeng +2
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated it…
Positional Contrastive Learning for Volumetric Medical Image Segmentation
Dewen Zeng, Yawen Wu, Xinrong Hu +6
The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because o…