A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation
We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-
modality medical image segmentation, aiming to perform segmentation on the unannotated …
modality medical image segmentation, aiming to perform segmentation on the unannotated …
Data efficient unsupervised domain adaptation for cross-modality image segmentation
Deep learning models trained on medical images from a source domain (eg eg imaging
modality) often fail when deployed on images from a different target domain, despite …
modality) often fail when deployed on images from a different target domain, despite …
Deep symmetric adaptation network for cross-modality medical image segmentation
Unsupervised domain adaptation (UDA) methods have shown their promising performance
in the cross-modality medical image segmentation tasks. These typical methods usually …
in the cross-modality medical image segmentation tasks. These typical methods usually …
Reducing domain gap in frequency and spatial domain for cross-modality domain adaptation on medical image segmentation
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain
and performs well on unlabeled target domain. In medical image segmentation field, most …
and performs well on unlabeled target domain. In medical image segmentation field, most …
Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation
Unsupervised domain adaptation has increasingly gained interest in medical image
computing, aiming to tackle the performance degradation of deep neural networks when …
computing, aiming to tackle the performance degradation of deep neural networks when …
A new bidirectional unsupervised domain adaptation segmentation framework
Abstract Domain shift happens in cross-domain scenarios commonly because of the wide
gaps between different domains: when applying a deep learning model well-trained in one …
gaps between different domains: when applying a deep learning model well-trained in one …
SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation
Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
A 3D Anatomy-Guided Self-Training Segmentation Framework for Unpaired Cross-Modality Medical Image Segmentation
Y Zhuang, H Liu, E Song, X Xu, Y Liao… - … on Radiation and …, 2023 - ieeexplore.ieee.org
Unsupervised domain adaptation (UDA) methods have achieved promising performance in
alleviating the domain shift between different imaging modalities. In this article, we propose …
alleviating the domain shift between different imaging modalities. In this article, we propose …
Unsupervised cross-modality adaptation via dual structural-oriented guidance for 3D medical image segmentation
J Xian, X Li, D Tu, S Zhu, C Zhang, X Liu… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Deep convolutional neural networks (CNNs) have achieved impressive performance in
medical image segmentation; however, their performance could degrade significantly when …
medical image segmentation; however, their performance could degrade significantly when …
A structure-aware framework of unsupervised cross-modality domain adaptation via frequency and spatial knowledge distillation
Unsupervised domain adaptation (UDA) aims to train a model on a labeled source domain
and adapt it to an unlabeled target domain. In medical image segmentation field, most …
and adapt it to an unlabeled target domain. In medical image segmentation field, most …