Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …
imaging. However, these approaches primarily focus on supervised learning, assuming that …
Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels
The contrastive pre-training of a recognition model on a large dataset of unlabeled data
often boosts the model's performance on downstream tasks like image classification …
often boosts the model's performance on downstream tasks like image classification …
Advances in Deep Learning Models for Resolving Medical Image Segmentation Data Scarcity Problem: A Topical Review
AK Upadhyay, AK Bhandari - Archives of Computational Methods in …, 2024 - Springer
Deep learning (DL) methods have recently become state-of-the-art in most automated
medical image segmentation tasks. Some of the biggest challenges in this field are related …
medical image segmentation tasks. Some of the biggest challenges in this field are related …
Unsupervised domain adaptation via style adaptation and boundary enhancement for medical semantic segmentation
Y Ge, ZM Chen, G Zhang, AA Heidari, H Chen, S Teng - Neurocomputing, 2023 - Elsevier
The objective of semantic segmentation in cross-modal medicine is to align the distribution
among different domains. The images from different domains contain various styles and …
among different domains. The images from different domains contain various styles and …
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 …
Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation
Medical image segmentation methods based on deep learning have made remarkable
progress. However, such existing methods are sensitive to data distribution. Therefore, slight …
progress. However, such existing methods are sensitive to data distribution. Therefore, slight …
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 …
Sgt: Scene graph-guided transformer for surgical report generation
The robotic surgical report reflects the operations during surgery and relates to the
subsequent treatment. Therefore, it is especially important to generate accurate surgical …
subsequent treatment. Therefore, it is especially important to generate accurate surgical …
[HTML][HTML] MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets
S Kuang, HC Woodruff, R Granzier, TJA van Nijnatten… - Neural Networks, 2023 - Elsevier
Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging
(MRI) has received increased attention in the last decade, however, the domain shift which …
(MRI) has received increased attention in the last decade, however, the domain shift which …
Attention-enhanced disentangled representation learning for unsupervised domain adaptation in cardiac segmentation
To overcome the barriers of multimodality and scarcity of annotations in medical image
segmentation, many unsupervised domain adaptation (UDA) methods have been proposed …
segmentation, many unsupervised domain adaptation (UDA) methods have been proposed …