Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
[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 …
AGGN: Attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion
In this paper, a magnetic resonance imaging (MRI) oriented novel attention-based glioma
grading network (AGGN) is proposed. By applying the dual-domain attention mechanism …
grading network (AGGN) is proposed. By applying the dual-domain attention mechanism …
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 …
Multi-constraint generative adversarial network for dose prediction in radiotherapy
B Zhan, J Xiao, C Cao, X Peng, C Zu, J Zhou… - Medical Image …, 2022 - Elsevier
Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to
deliver an accurate dose to the planning target volume (PTV) while protecting the …
deliver an accurate dose to the planning target volume (PTV) while protecting the …
Medical image segmentation on mri images with missing modalities: A review
R Azad, N Khosravi, M Dehghanmanshadi… - arXiv preprint arXiv …, 2022 - arxiv.org
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
their negative repercussions is considered a hurdle in biomedical imaging. The combination …
Swin transformer-based GAN for multi-modal medical image translation
Medical image-to-image translation is considered a new direction with many potential
applications in the medical field. The medical image-to-image translation is dominated by …
applications in the medical field. The medical image-to-image translation is dominated by …
Multi-modal MRI image synthesis via GAN with multi-scale gate mergence
B Zhan, D Li, X Wu, J Zhou… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Multi-modal magnetic resonance imaging (MRI) plays a critical role in clinical diagnosis and
treatment nowadays. Each modality of MRI presents its own specific anatomical features …
treatment nowadays. Each modality of MRI presents its own specific anatomical features …
Edge-preserving MRI image synthesis via adversarial network with iterative multi-scale fusion
Magnetic resonance imaging (MRI) is a major imaging technique for studying
neuroanatomy. By applying different pulse sequences and parameters, different modalities …
neuroanatomy. By applying different pulse sequences and parameters, different modalities …
Review of Disentanglement Approaches for Medical Applications--Towards Solving the Gordian Knot of Generative Models in Healthcare
J Fragemann, L Ardizzone, J Egger… - arXiv preprint arXiv …, 2022 - arxiv.org
Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …
segmentation, or classification. Besides this, they are often criticized as black boxes as their …