Current challenges of implementing artificial intelligence in medical imaging
The idea of using artificial intelligence (AI) in medical practice has gained vast interest due
to its potential to revolutionise healthcare systems. However, only some AI algorithms are …
to its potential to revolutionise healthcare systems. However, only some AI algorithms are …
Machine learning empowering personalized medicine: A comprehensive review of medical image analysis methods
Artificial intelligence (AI) advancements, especially deep learning, have significantly
improved medical image processing and analysis in various tasks such as disease …
improved medical image processing and analysis in various tasks such as disease …
Evaluating synthetic medical images using artificial intelligence with the GAN algorithm
In recent years, considerable work has been conducted on the development of synthetic
medical images, but there are no satisfactory methods for evaluating their medical suitability …
medical images, but there are no satisfactory methods for evaluating their medical suitability …
Seeing plants as never before
Imaging has long supported our ability to understand the inner life of plants, their
development, and response to a dynamic environment. While optical microscopy remains …
development, and response to a dynamic environment. While optical microscopy remains …
Deep learning based MRI reconstruction with transformer
Z Wu, W Liao, C Yan, M Zhao, G Liu, N Ma… - Computer Methods and …, 2023 - Elsevier
Magnetic resonance imaging (MRI) has become one of the most powerful imaging
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip
Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems
have been developed rapidly in the past decade and found important applications in drug …
have been developed rapidly in the past decade and found important applications in drug …
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Lately, deep learning technology has been extensively investigated for accelerating
dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved …
dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved …
Machine Learning and Deep Learning Applications in Magnetic Particle Imaging
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging
technique depicting high sensitivity and spatial resolution. It originated in the early 2000s …
technique depicting high sensitivity and spatial resolution. It originated in the early 2000s …
Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques
C Otgonbaatar, PH Jeon, JK Ryu, H Shim… - Acta …, 2023 - journals.sagepub.com
Background The reference protocol for the quantification of coronary artery calcium (CAC)
should be updated to meet the standards of modern imaging techniques. Purpose To assess …
should be updated to meet the standards of modern imaging techniques. Purpose To assess …
Modern image-guided surgery: A narrative review of medical image processing and visualization
Z Lin, C Lei, L Yang - Sensors, 2023 - mdpi.com
Medical image analysis forms the basis of image-guided surgery (IGS) and many of its
fundamental tasks. Driven by the growing number of medical imaging modalities, the …
fundamental tasks. Driven by the growing number of medical imaging modalities, the …