Deep semi-supervised learning for medical image segmentation: A review
Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …
vision tasks. However, in many practical applications, large-scale labeled datasets are not …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in
hospitals across the world. These have the potential to revolutionize our understanding of …
hospitals across the world. These have the potential to revolutionize our understanding of …
[HTML][HTML] A survey of emerging applications of diffusion probabilistic models in mri
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …
a gradual sampling process to synthesize data, have gained increasing research interest …
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
MR Hernandez Petzsche, E de la Rosa, U Hanning… - Scientific data, 2022 - nature.com
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer
based automated medical image processing is increasingly finding its way into clinical …
based automated medical image processing is increasingly finding its way into clinical …
Association of brain age, lesion volume, and functional outcome in patients with stroke
Background and Objectives Functional outcomes after stroke are strongly related to focal
injury measures. However, the role of global brain health is less clear. In this study, we …
injury measures. However, the role of global brain health is less clear. In this study, we …
A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke
To extract meaningful and reproducible models of brain function from stroke images, for both
clinical and research proposes, is a daunting task severely hindered by the great variability …
clinical and research proposes, is a daunting task severely hindered by the great variability …
BrainSegFounder: towards 3D foundation models for neuroimage segmentation
The burgeoning field of brain health research increasingly leverages artificial intelligence
(AI) to analyze and interpret neuroimaging data. Medical foundation models have shown …
(AI) to analyze and interpret neuroimaging data. Medical foundation models have shown …
Improving structural MRI preprocessing with hybrid transformer GANs
O Grigas, R Maskeliūnas, R Damaševičius - Life, 2023 - mdpi.com
Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate
any pathologies in the human body. One of the areas of interest is the human brain …
any pathologies in the human body. One of the areas of interest is the human brain …
[HTML][HTML] SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization
There are considerable interests in automatic stroke lesion segmentation on magnetic
resonance (MR) images in the medical imaging field, as stroke is an important …
resonance (MR) images in the medical imaging field, as stroke is an important …