Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Advances in medical image analysis with vision transformers: a comprehensive review
The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …
has recently also triggered broad interest in Computer Vision. Among other merits …
ReconFormer: Accelerated MRI reconstruction using recurrent transformer
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
Transformers in healthcare: A survey
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …
healthcare, the adoption of the Transformers neural network architecture is rapidly changing …
Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
Iterative residual optimization network for limited-angle tomographic reconstruction
Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …
leading to edge divergence with degraded image quality. Recently, deep learning has been …
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
While enabling accelerated acquisition and improved reconstruction accuracy, current deep
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
Vision transformers in medical imaging: A review
EU Henry, O Emebob, CA Omonhinmin - arXiv preprint arXiv:2211.10043, 2022 - arxiv.org
Transformer, a model comprising attention-based encoder-decoder architecture, have
gained prevalence in the field of natural language processing (NLP) and recently influenced …
gained prevalence in the field of natural language processing (NLP) and recently influenced …
DuDoUFNet: dual-domain under-to-fully-complete progressive restoration network for simultaneous metal artifact reduction and low-dose CT reconstruction
To reduce the potential risk of radiation to the patient, low-dose computed tomography
(LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional …
(LDCT) has been widely adopted in clinical practice for reconstructing cross-sectional …
A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …