Domain adaptation for medical image analysis: a survey
Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …
from the domain shift problem caused by different distributions between source/reference …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
[HTML][HTML] Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results
Deep learning models have shown their advantage in many different tasks, including
neuroimage analysis. However, to effectively train a high-quality deep learning model, the …
neuroimage analysis. However, to effectively train a high-quality deep learning model, the …
CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research
field for many years. In the last decade, intensive developments in deep learning (DL) …
field for many years. In the last decade, intensive developments in deep learning (DL) …
Pseudo-label guided contrastive learning for semi-supervised medical image segmentation
Although recent works in semi-supervised learning (SemiSL) have accomplished significant
success in natural image segmentation, the task of learning discriminative representations …
success in natural image segmentation, the task of learning discriminative representations …
[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
A survey on incorporating domain knowledge into deep learning for medical image analysis
Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
analysis, the small size of medical datasets remains a major bottleneck in this area. To …
[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation
Abstract Domain Adaptation (DA) has recently been of strong interest in the medical imaging
community. While a large variety of DA techniques have been proposed for image …
community. While a large variety of DA techniques have been proposed for image …
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 …