A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Multi-scale self-guided attention for medical image segmentation
Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
segmentation, standard models still have some drawbacks. First, the use of multi-scale …
Autoencoder based self-supervised test-time adaptation for medical image analysis
Deep neural networks have been successfully applied to medical image analysis tasks like
segmentation and synthesis. However, even if a network is trained on a large dataset from …
segmentation and synthesis. However, even if a network is trained on a large dataset from …
Application of artificial intelligence in pediatrics: past, present and future
LQ Shu, YK Sun, LH Tan, Q Shu, AC Chang - World Journal of Pediatrics, 2019 - Springer
Artificial intelligence (AI) is a very active computer science research field aiming to develop
systems that mimic human intelligence and is helpful in many human activities, including …
systems that mimic human intelligence and is helpful in many human activities, including …
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive
volumetric studies and quantitative analysis of early brain development. However …
volumetric studies and quantitative analysis of early brain development. However …
Genetic patterning for child psychopathology is distinct from that for adults and implicates fetal cerebellar development
Childhood psychiatric symptoms are often diffuse but can coalesce into discrete mental
illnesses during late adolescence. We leveraged polygenic scores (PGSs) to parse genomic …
illnesses during late adolescence. We leveraged polygenic scores (PGSs) to parse genomic …
[HTML][HTML] CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation
Quantifying the volume of the cerebellum and its lobes is of profound interest in various
neurodegenerative and acquired diseases. Especially for the most common spinocerebellar …
neurodegenerative and acquired diseases. Especially for the most common spinocerebellar …
Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Purpose Precise segmentation of bladder walls and tumor regions is an essential step
toward noninvasive identification of tumor stage and grade, which is critical for treatment …
toward noninvasive identification of tumor stage and grade, which is critical for treatment …
[HTML][HTML] Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization
The cerebellum plays a central role in sensory input, voluntary motor action, and many
neuropsychological functions and is involved in many brain diseases and neurological …
neuropsychological functions and is involved in many brain diseases and neurological …
Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age
Few neuroimaging studies are sufficiently large to adequately describe population‐wide
variations. This study's primary aim was to generate neuroanatomical norms and individual …
variations. This study's primary aim was to generate neuroanatomical norms and individual …