Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
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 …
[HTML][HTML] Brain charts for the human lifespan
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research
and clinical studies of the human brain. However, no reference standards currently exist to …
and clinical studies of the human brain. However, no reference standards currently exist to …
[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
Fedbn: Federated learning on non-iid features via local batch normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …
deep models on the network edge without centrally aggregating raw data and hence …
Preparing medical imaging data for machine learning
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …
potential applications are vast and include the entirety of the medical imaging life cycle from …
Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD)
are not well understood. Using a large neuroimaging dataset, we identified three latent …
are not well understood. Using a large neuroimaging dataset, we identified three latent …
Hippocampal contributions to social and cognitive deficits in autism spectrum disorder
Autism spectrum disorder (ASD) is characterized by hallmark impairments in social
functioning. Nevertheless, nonsocial cognition, including hippocampus-dependent spatial …
functioning. Nevertheless, nonsocial cognition, including hippocampus-dependent spatial …
Graph-based deep learning for medical diagnosis and analysis: past, present and future
With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …
problems have been tackled. It has become critical to explore how machine learning and …
Voxelmorph: a learning framework for deformable medical image registration
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …
image registration. Traditional registration methods optimize an objective function for each …