A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond
Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …
registration over the past decade. The initial developments, such as regression-based and U …
[HTML][HTML] Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia
Accurate and realistic simulation of high-dimensional medical images has become an
important research area relevant to many AI-enabled healthcare applications. However …
important research area relevant to many AI-enabled healthcare applications. However …
Fast three‐dimensional image generation for healthy brain aging using diffeomorphic registration
Predicting brain aging can help in the early detection and prognosis of neurodegenerative
diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance …
diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance …
Generative aging of brain MR-images and prediction of Alzheimer progression
V Wegmayr, M Hörold, JM Buhmann - Pattern Recognition: 41st DAGM …, 2019 - Springer
Predicting the age progression of individual brain images from longitudinal data has been a
challenging problem, while its solution is considered key to improve dementia prognosis …
challenging problem, while its solution is considered key to improve dementia prognosis …
Longitudinal brain MR image modeling using personalized memory for Alzheimer's disease
Longitudinal analysis of a disease is an important issue to understand its progression and
design prognosis and early diagnostic tools. From the longitudinal images where data is …
design prognosis and early diagnostic tools. From the longitudinal images where data is …
Deep modeling of growth trajectories for longitudinal prediction of missing infant cortical surfaces
Charting cortical growth trajectories is of paramount importance for understanding brain
development. However, such analysis necessitates the collection of longitudinal data, which …
development. However, such analysis necessitates the collection of longitudinal data, which …
Longitudinal structural MRI data prediction in nondemented and demented older adults via generative adversarial convolutional network
L Song, Q Wang, H Li, J Fan, B Hu - Neural Processing Letters, 2023 - Springer
Alzheimer's disease (AD) is the most common cause of dementia and threatens the health of
millions of people. Early stage diagnosis of AD is critical for improving clinical outcomes and …
millions of people. Early stage diagnosis of AD is critical for improving clinical outcomes and …
Y-Net: biomedical image segmentation and clustering
S Pathan, A Tripathi - arXiv preprint arXiv:2004.05698, 2020 - arxiv.org
We propose a deep clustering architecture alongside image segmentation for medical
image analysis. The main idea is based on unsupervised learning to cluster images on …
image analysis. The main idea is based on unsupervised learning to cluster images on …
Diffeomorphic autoencoders for lddmm atlas building
In this work, we present an example of the integration of conventional global and
diffeomorphic image registration methods with deep learning. Our method employs a form of …
diffeomorphic image registration methods with deep learning. Our method employs a form of …