The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
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 …
Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network
Synthesized medical images have several important applications, eg, as an intermedium in
cross-modality image registration and as supplementary training samples to boost the …
cross-modality image registration and as supplementary training samples to boost the …
Image synthesis in multi-contrast MRI with conditional generative adversarial networks
Acquiring images of the same anatomy with multiple different contrasts increases the
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
diversity of diagnostic information available in an MR exam. Yet, the scan time limitations …
Generative adversarial perturbations
In this paper, we propose novel generative models for creating adversarial examples,
slightly perturbed images resembling natural images but maliciously crafted to fool pre …
slightly perturbed images resembling natural images but maliciously crafted to fool pre …
Synseg-net: Synthetic segmentation without target modality ground truth
A key limitation of deep convolutional neural network (DCNN)-based image segmentation
methods is the lack of generalizability. Manually traced training images are typically required …
methods is the lack of generalizability. Manually traced training images are typically required …
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 …
Adversarial image synthesis for unpaired multi-modal cardiac data
This paper demonstrates the potential for synthesis of medical images in one modality (eg
MR) from images in another (eg CT) using a CycleGAN [24] architecture. The synthesis can …
MR) from images in another (eg CT) using a CycleGAN [24] architecture. The synthesis can …
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 …