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 …

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

J Li, J Chen, Y Tang, C Wang, BA Landman… - Medical image …, 2023 - Elsevier
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

[HTML][HTML] Brain charts for the human lifespan

RAI Bethlehem, J Seidlitz, SR White, JW Vogel… - Nature, 2022 - nature.com
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 …

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

AM Buch, PE Vértes, J Seidlitz, SH Kim… - Nature …, 2023 - nature.com
The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD)
are not well understood. Using a large neuroimaging dataset, we identified three latent …

Fedbn: Federated learning on non-iid features via local batch normalization

X Li, M Jiang, X Zhang, M Kamp, Q Dou - arXiv preprint arXiv:2102.07623, 2021 - arxiv.org
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 …

Preparing medical imaging data for machine learning

MJ Willemink, WA Koszek, C Hardell, J Wu… - Radiology, 2020 - pubs.rsna.org
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 …

[HTML][HTML] Graph-based deep learning for medical diagnosis and analysis: past, present and future

D Ahmedt-Aristizabal, MA Armin, S Denman, C Fookes… - Sensors, 2021 - mdpi.com
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 …

Voxelmorph: a learning framework for deformable medical image registration

G Balakrishnan, A Zhao, MR Sabuncu… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …

[HTML][HTML] Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review

FV Farahani, W Karwowski, NR Lighthall - frontiers in Neuroscience, 2019 - frontiersin.org
Background: Analysis of the human connectome using functional magnetic resonance
imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to …

[图书][B] Deep learning on graphs

Y Ma, J Tang - 2021 - books.google.com
Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …