[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities
Gliomas are one of the most prevalent types of primary brain tumors, accounting for more
than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the …
than 30% of all cases and they develop from the glial stem or progenitor cells. In theory, the …
One model to synthesize them all: Multi-contrast multi-scale transformer for missing data imputation
Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical practice as each
contrast provides complementary information. However, the availability of each imaging …
contrast provides complementary information. However, the availability of each imaging …
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …
improve learning of generalizable synthesis models that reliably translate source-onto target …
Deep learning‐based convolutional neural network for intramodality brain MRI synthesis
Purpose The existence of multicontrast magnetic resonance (MR) images increases the
level of clinical information available for the diagnosis and treatment of brain cancer …
level of clinical information available for the diagnosis and treatment of brain cancer …
Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN
Simple Summary Contrast-enhanced MR has been used in diagnosing and treating liver
patients. Recently, development in MR-guided radiation therapy calls for daily contrast MR …
patients. Recently, development in MR-guided radiation therapy calls for daily contrast MR …
Metal Particle Detection by Integration of a Generative Adversarial Network and Electrical Impedance Tomography (GAN-EIT) for a Wet-Type Gravity Vibration …
The minor copper (Cu) particles among major aluminum (Al) particles have been detected
by means of an integration of a generative adversarial network and electrical impedance …
by means of an integration of a generative adversarial network and electrical impedance …
Deep learning segmentation of non-perfusion area from color fundus images and AI-generated fluorescein angiography
K Masayoshi, Y Katada, N Ozawa, M Ibuki, K Negishi… - Scientific Reports, 2024 - nature.com
The non-perfusion area (NPA) of the retina is an important indicator in the visual prognosis
of patients with branch retinal vein occlusion (BRVO). However, the current evaluation …
of patients with branch retinal vein occlusion (BRVO). However, the current evaluation …
Synthesizing contrast-enhanced computed tomography images with an improved conditional generative adversarial network
Contrast-enhanced computed tomography (CE-CT) images are used extensively for the
diagnosis of liver cancer in clinical practice. Compared with the non-contrast CT (NC-CT) …
diagnosis of liver cancer in clinical practice. Compared with the non-contrast CT (NC-CT) …
[HTML][HTML] Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI
Abstract T1-and T2-weighted (T1w and T2w) images are essential for tissue classification
and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However …
and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However …