A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning
This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
[HTML][HTML] Multi-modality cardiac image computing: A survey
Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …
cardiovascular diseases. It allows a combination of complementary anatomical …
Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer
Identifying anatomical correspondences in the human brain throughout the lifespan is an
essential prerequisite for studying brain development and aging. But given the tremendous …
essential prerequisite for studying brain development and aging. But given the tremendous …
[HTML][HTML] Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis
AW Zwijnen, L Watzema, Y Ridwan… - Computers in Biology …, 2024 - Elsevier
Background Methods to monitor cardiac functioning non-invasively can accelerate
preclinical and clinical research into novel treatment options for heart failure. However …
preclinical and clinical research into novel treatment options for heart failure. However …
Unsupervised deep consistency learning adaptation network for cardiac cross-modality structural segmentation
D Li, Y Peng, J Sun, Y Guo - Medical & Biological Engineering & …, 2023 - Springer
Deep neural networks have recently been succeessful in the field of medical image
segmentation; however, they are typically subject to performance degradation problems …
segmentation; however, they are typically subject to performance degradation problems …
A regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation
Q Wang, X Li, M Chen, L Chen… - Physics in Medicine & …, 2022 - iopscience.iop.org
Objective. A semi-supervised learning method is an essential tool for applying medical
image segmentation. However, the existing semi-supervised learning methods rely heavily …
image segmentation. However, the existing semi-supervised learning methods rely heavily …