A review of medical image data augmentation techniques for deep learning applications
Research in artificial intelligence for radiology and radiotherapy has recently become
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
increasingly reliant on the use of deep learning‐based algorithms. While the performance of …
Domain generalization: A survey
Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …
challenging for machines to reproduce. This is because most learning algorithms strongly …
Domain adaptation for medical image analysis: a survey
H Guan, M Liu - IEEE Transactions on Biomedical Engineering, 2021 - ieeexplore.ieee.org
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 …
Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space
Federated learning allows distributed medical institutions to collaboratively learn a shared
prediction model with privacy protection. While at clinical deployment, the models trained in …
prediction model with privacy protection. While at clinical deployment, the models trained in …
Data augmentation for medical imaging: A systematic literature review
Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …
diverse training sets. However, collecting large datasets for medical imaging is still a …
Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
[HTML][HTML] SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …
considerably larger than the size of any research dataset. Therefore, the ability to analyze …
Shape-aware meta-learning for generalizing prostate MRI segmentation to unseen domains
Abstract Model generalization capacity at domain shift (eg, various imaging protocols and
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …
Causality-inspired single-source domain generalization for medical image segmentation
Deep learning models usually suffer from the domain shift issue, where models trained on
one source domain do not generalize well to other unseen domains. In this work, we …
one source domain do not generalize well to other unseen domains. In this work, we …