Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation. Zixian Su, Kai Yao, Xi Yang ...
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation. Zixian Su, Kai Yao, Xi Yang, Qiufeng Wang, Yuyao Yan ...
Jan 18, 2021 · ... segmentation method for medical images ... Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation.
A Structure-Aware Framework of Unsupervised Cross-Modality ...
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May 10, 2024 · 2023, IEEE Transactions on Medical Imaging. Mind the Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation.
Given the diversity of medical images, traditional image segmentation models face the issue of domain shift. Unsupervised domain adaptation (UDA) methods ...
Missing: Mind | Show results with:Mind
of multi-modal contrastive learning. Paired inputs from two modalities (e.g., image-caption) are sampled from the dataset and embedded into the hypersphere ...
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation [arXiv on 24 Jan 2019]; Unsupervised domain ...
Apr 28, 2023 · ... modal cardiac MRI unsupervised ... Contrastive learning of global and local features for medical image segmentation with limited annotations.
Deep learning based approaches have achieved great success on the automatic cardiac image segmentation task. However, the achieved segmentation performance ...
Missing: Mind Gap: Local Medical
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation ... C2KD: Bridging the Modality Gap for Cross-Modal Knowledge ...