Anomaly detection in medical imaging-a mini review
The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …
improvements in detecting, visualizing and segmenting lesions, easing the workload for …
Unsupervised medical image translation with adversarial diffusion models
Imputation of missing images via source-to-target modality translation can improve diversity
in medical imaging protocols. A pervasive approach for synthesizing target images involves …
in medical imaging protocols. A pervasive approach for synthesizing target images involves …
ResViT: residual vision transformers for multimodal medical image synthesis
Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …
recently been established as state-of-the-art in numerous medical image synthesis tasks …
Multimodal MR synthesis via modality-invariant latent representation
We propose a multi-input multi-output fully convolutional neural network model for MRI
synthesis. The model is robust to missing data, as it benefits from, but does not require …
synthesis. The model is robust to missing data, as it benefits from, but does not require …
Missing MRI pulse sequence synthesis using multi-modal generative adversarial network
Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and
plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in …
plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in …
[HTML][HTML] White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also
frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The …
frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The …
mustGAN: multi-stream generative adversarial networks for MR image synthesis
Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
[HTML][HTML] DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …
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 …
Unified multi-modal image synthesis for missing modality imputation
Multi-modal medical images provide complementary soft-tissue characteristics that aid in the
screening and diagnosis of diseases. However, limited scanning time, image corruption and …
screening and diagnosis of diseases. However, limited scanning time, image corruption and …