Search Results for author:
Found 11 papers, 4 papers with code
Deep Co-Training for Semi-Supervised Image Segmentation
In this paper, we aim to improve the performance of semantic image segmentation in a semi-supervised setting in which training is effectuated with a reduced set of annotated images and additional non-annotated images.
Discretely-constrained deep network for weakly supervised segmentation
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions.
Boosting Image Recognition with Non-differentiable Constraints
In this paper, we study the problem of image recognition with non-differentiable constraints.
Information based Deep Clustering: An experimental study
The second, named Invariant Information Clustering (IIC), maximizes the mutual information between the clustering of a sample and its geometrically transformed version.
Mutual information deep regularization for semi-supervised segmentation
The scarcity of labeled data often limits the application of deep learning to medical image segmentation.
Self-paced and self-consistent co-training for semi-supervised image segmentation
Moreover, to encourage predictions from different networks to be both consistent and confident, we enhance this generalized JSD loss with an uncertainty regularizer based on entropy.
Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization
In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network.
Context-aware virtual adversarial training for anatomically-plausible segmentation
Despite their outstanding accuracy, semi-supervised segmentation methods based on deep neural networks can still yield predictions that are considered anatomically impossible by clinicians, for instance, containing holes or disconnected regions.
Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
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 classification.
Diversified Multi-prototype Representation for Semi-supervised Segmentation
This work considers semi-supervised segmentation as a dense prediction problem based on prototype vector correlation and proposes a simple way to represent each segmentation class with multiple prototypes.
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation
Unsupervised pre-training has been proven as an effective approach to boost various downstream tasks given limited labeled data.
