Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

S Kumari, P Singh - Computers in Biology and Medicine, 2024 - Elsevier
Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification

Z Jiao, X Peng, Y Wang, J Xiao, D Nie, X Wu… - Medical Image …, 2023 - Elsevier
Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy
treatment plan is always based on a high-quality dose distribution map which is produced by …

Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification

Y Fang, M Wang, GG Potter, M Liu - Medical image analysis, 2023 - Elsevier
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

S Zhang, J Yang, Y Zhang, J Zhong, W Hu, C Li… - Brain Sciences, 2023 - mdpi.com
Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human
health all over the world. It is of great importance to diagnose ND through combining artificial …

Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification

Y Ma, C Zhang, X Wang, Q Wang, L Cao… - arXiv preprint arXiv …, 2024 - arxiv.org
Major depressive disorder (MDD) is a common mental disorder that typically affects a
person's mood, cognition, behavior, and physical health. Resting-state functional magnetic …