[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
3d brain and heart volume generative models: A survey
Generative models such as generative adversarial networks and autoencoders have gained
a great deal of attention in the medical field due to their excellent data generation capability …
a great deal of attention in the medical field due to their excellent data generation capability …
Conditional GAN with 3D discriminator for MRI generation of Alzheimer's disease progression
Many studies aim to predict the degree of deformation on affected brain regions as
Alzheimer's disease (AD) progresses. However, those studies have been often limited since …
Alzheimer's disease (AD) progresses. However, those studies have been often limited since …
[HTML][HTML] Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning
Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
innate traits of irreversibility with subtle and gradual progression. These characteristics make …
Learning to synthesise the ageing brain without longitudinal data
How will my face look when I get older? Or, for a more challenging question: How will my
brain look when I get older? To answer this question one must devise (and learn from data) …
brain look when I get older? To answer this question one must devise (and learn from data) …
Invertible modeling of bidirectional relationships in neuroimaging with normalizing flows: application to brain aging
Many machine learning tasks in neuroimaging aim at modeling complex relationships
between a brain's morphology as seen in structural MR images and clinical scores and …
between a brain's morphology as seen in structural MR images and clinical scores and …
[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 …
Longitudinal self-supervision to disentangle inter-patient variability from disease progression
The problem of building disease progression models with longitudinal data has long been
addressed with parametric mixed-effect models. They provide interpretable models at the …
addressed with parametric mixed-effect models. They provide interpretable models at the …
Generative image transformer (GIT): unsupervised continuous image generative and transformable model for [123I]FP-CIT SPECT images
S Watanabe, T Ueno, Y Kimura, M Mishina… - Annals of nuclear …, 2021 - Springer
Objective Recently, generative adversarial networks began to be actively studied in the field
of medical imaging. These models are used for augmenting the variation of images to …
of medical imaging. These models are used for augmenting the variation of images to …
Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)
Abstract Diabetic Macular Edema (DME) represents a significant visual impairment among
individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially …
individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially …