Pdam: A panoptic-level feature alignment framework for unsupervised domain adaptive instance segmentation in microscopy images
In this work, we present an unsupervised domain adaptation (UDA) method, named
Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation …
Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation …
Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting
Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for
digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift …
digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift …
Darcnn: Domain adaptive region-based convolutional neural network for unsupervised instance segmentation in biomedical images
In the biomedical domain, there is an abundance of dense, complex data where objects of
interest may be challenging to detect or constrained by limits of human knowledge. Labelled …
interest may be challenging to detect or constrained by limits of human knowledge. Labelled …
Unsupervised domain adaptation for nuclei segmentation: adapting from hematoxylin & eosin stained slides to immunohistochemistry stained slides using a …
Background and objective Unsupervised domain adaptation (UDA) is a powerful approach
in tackling domain discrepancies and reducing the burden of laborious and error-prone pixel …
in tackling domain discrepancies and reducing the burden of laborious and error-prone pixel …
Domain adaptive nuclei instance segmentation and classification via category-aware feature alignment and pseudo-labelling
Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the
models' adaptation ability in general computer vision. However, different from the natural …
models' adaptation ability in general computer vision. However, different from the natural …
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
While deep learning methods hitherto have achieved considerable success in medical
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …
Birds of a feather flock together: Category-divergence guidance for domain adaptive segmentation
Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a
certain model from a source domain to a target domain. Present UDA models focus on …
certain model from a source domain to a target domain. Present UDA models focus on …
Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training
Unsupervised domain adaption (UDA), which aims to enhance the segmentation
performance of deep models on unlabeled data, has recently drawn much attention. In this …
performance of deep models on unlabeled data, has recently drawn much attention. In this …
Scale variance minimization for unsupervised domain adaptation in image segmentation
We focus on unsupervised domain adaptation (UDA) in image segmentation. Existing works
address this challenge largely by aligning inter-domain representations, which may lead …
address this challenge largely by aligning inter-domain representations, which may lead …
Semi-supervised domain adaptive medical image segmentation through consistency regularized disentangled contrastive learning
Although unsupervised domain adaptation (UDA) is a promising direction to alleviate
domain shift, they fall short of their supervised counterparts. In this work, we investigate …
domain shift, they fall short of their supervised counterparts. In this work, we investigate …