A review of multimodal image matching: Methods and applications
Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …
similar structure/content from two or more images that are of significant modalities or …
Deep learning in medical image registration: a review
This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …
methods. We summarized the latest developments and applications of DL-based registration …
Deep learning in medical image registration: a survey
The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
Medical image registration using deep neural networks: a comprehensive review
Image-guided interventions are saving the lives of a large number of patients where the
image registration should indeed be considered as the most complex and complicated issue …
image registration should indeed be considered as the most complex and complicated issue …
A review of deep learning-based deformable medical image registration
The alignment of images through deformable image registration is vital to clinical
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …
applications (eg, atlas creation, image fusion, and tumor targeting in image-guided …
Non-rigid image registration using self-supervised fully convolutional networks without training data
A novel non-rigid image registration algorithm is built upon fully convolutional networks
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
COVID-19 automatic diagnosis with radiographic imaging: Explainable attention transfer deep neural networks
Researchers seek help from deep learning methods to alleviate the enormous burden of
reading radiological images by clinicians during the COVID-19 pandemic. However …
reading radiological images by clinicians during the COVID-19 pandemic. However …
Recent advancements and future prospects in active deep learning for medical image segmentation and classification
Medical images are helpful for the diagnosis, treatment, and evaluation of diseases. Precise
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
medical image segmentation improves diagnosis and decision-making, aiding intelligent …
A coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve
fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless …
fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless …
Artificial intelligence with deep learning in nuclear medicine and radiology
The use of deep learning in medical imaging has increased rapidly over the past few years,
finding applications throughout the entire radiology pipeline, from improved scanner …
finding applications throughout the entire radiology pipeline, from improved scanner …