Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
A foundation model for generalizable disease detection from retinal images
Medical artificial intelligence (AI) offers great potential for recognizing signs of health
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
conditions in retinal images and expediting the diagnosis of eye diseases and systemic …
Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …
collides with machine learning applications in the field of medical imaging analysis and …
An overview of deep learning methods for multimodal medical data mining
Deep learning methods have achieved significant results in various fields. Due to the
success of these methods, many researchers have used deep learning algorithms in …
success of these methods, many researchers have used deep learning algorithms in …
On the challenges and perspectives of foundation models for medical image analysis
This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …
pretrained models, ie, foundation models, which promise to significantly improve the …
Self-supervised driven consistency training for annotation efficient histopathology image analysis
Training a neural network with a large labeled dataset is still a dominant paradigm in
computational histopathology. However, obtaining such exhaustive manual annotations is …
computational histopathology. However, obtaining such exhaustive manual annotations is …
Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis
The automatic diagnosis of various conventional ophthalmic diseases from fundus images is
important in clinical practice. However, developing such automatic solutions is challenging …
important in clinical practice. However, developing such automatic solutions is challenging …
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
While deep learning methods hitherto have achieved considerable success in medical
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …
image segmentation, they are still hampered by two limitations:(i) reliance on large-scale …