Automatic and accurate segmentation of anatomical structures on medical images is crucial for detecting various potential diseases.
In this work, we propose an uncertainty-aware domain alignment framework to address the domain shift problem in the cross-domain Unsupervised Domain Adaptation ...
Missing: lab | Show results with:lab
In this work, we propose an uncertainty-aware domain alignment framework to address the domain shift problem in the cross-domain Unsupervised Domain Adaptation ...
In this work, we propose an uncertainty-aware domain alignment framework to address the domain shift problem in the cross-domain Unsupervised Domain Adaptation ...
Oct 6, 2024 · This work, for the 2024 CL-Detection MICCAI Challenge, proposes a domain alignment strategy with a regional facial extraction module and an X-ray artefact ...
Under unsupervised domain adaptation settings, we validate the effectiveness of this work by adapting our multi-organ segmentation model to two pathological ...
Feb 17, 2024 · Firstly, we trained the diffusion model and uncertainty-aware model using the source data with annotations. Secondly, we aligned one target ...
This work proposes a novel method to estimate segmentation uncertainty by leveraging global information from the segmentation masks.
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Sep 1, 2021 · This paper utilizes pseudo labeling scheme for domain adaptive mitochondria segmentation with experimental results outperforming some baseline ...
Missing: lab | Show results with:lab
An uncertainty-guided domain alignment method to aim at alleviating this problem to transfer discriminative knowledge across distinct domains and disigns a ...
Missing: aware anatomical lab