A unified visual information preservation framework for self-supervised pre-training in medical image analysis
Recent advances in self-supervised learning (SSL) in computer vision are primarily
comparative, whose goal is to preserve invariant and discriminative semantics in latent�…
comparative, whose goal is to preserve invariant and discriminative semantics in latent�…
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher…�- Proceedings of the�…, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
beneficial for self-supervised learning schemes in computer vision and medical imaging�…
Representation recovering for self-supervised pre-training on medical images
Advances in self-supervised learning, especially in contrastive learning, have drawn
attention to investigating these techniques in providing effective visual representations from�…
attention to investigating these techniques in providing effective visual representations from�…
Preservational learning improves self-supervised medical image models by reconstructing diverse contexts
Preserving maximal information is the basic principle of designing self-supervised learning
methodologies. To reach this goal, contrastive learning adopts an implicit way which is�…
methodologies. To reach this goal, contrastive learning adopts an implicit way which is�…
Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?
Discriminative, restorative, and adversarial learning have proven beneficial for self-
supervised learning schemes in computer vision and medical imaging. Existing efforts�…
supervised learning schemes in computer vision and medical imaging. Existing efforts�…
Dive into the details of self-supervised learning for medical image analysis
Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific�…
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific�…
Unimiss: Universal medical self-supervised learning via breaking dimensionality barrier
Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that
is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D�…
is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D�…
Self pre-training with masked autoencoders for medical image classification and segmentation
Masked Autoencoder (MAE) has recently been shown to be effective in pre-training Vision
Transformers (ViT) for natural image analysis. By reconstructing full images from partially�…
Transformers (ViT) for natural image analysis. By reconstructing full images from partially�…
MsVRL: self-supervised multiscale visual representation learning via cross-level consistency for medical image segmentation
R Zheng, Y Zhong, S Yan, H Sun…�- IEEE Transactions on�…, 2022 - ieeexplore.ieee.org
Automated medical image segmentation for organs or lesions plays an essential role in
clinical diagnoses and treatment plannings. However, training an accurate and robust�…
clinical diagnoses and treatment plannings. However, training an accurate and robust�…
Emp-ssl: Towards self-supervised learning in one training epoch
Recently, self-supervised learning (SSL) has achieved tremendous success in learning
image representation. Despite the empirical success, most self-supervised learning methods�…
image representation. Despite the empirical success, most self-supervised learning methods�…