10 citations · 11 across the 5 of their papers we have counts for
9 papers
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation
Jizong Peng, Ping Wang, Marco Pedersoli +1
Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data. Among various methods, contrastive learning learns…
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
Jizong Peng, Ping Wang, Chrisitian Desrosiers +1
Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e.g., image…
Context-aware virtual adversarial training for anatomically-plausible segmentation
Ping Wang, Jizong Peng, Marco Pedersoli +3
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by cl…
Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization
Jizong Peng, Marco Pedersoli, Christian Desrosiers
The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images. Semi-supervised learning seeks to overcome this limitation by exp…
Self-paced and self-consistent co-training for semi-supervised image segmentation
Ping Wang, Jizong Peng, Marco Pedersoli +3
Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches for semi-su…
Boosting Image Recognition with Non-differentiable Constraints
Xuan Li, Yuchen Lu, Peng Xu +3
In this paper, we study the problem of image recognition with non-differentiable constraints. A lot of real-life recognition applications require a rich output structure with deter…