arXiv Computer Vision
L0-Norm-Based CycleGAN for Unpaired Brain CT-to-MR Synthesis
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Does DCGAN-Based Synthetic Augmentation Improve Brain Tumor MRI Classification? An Empirical Study
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arXiv AI
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Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis
arXiv:2606. 13341v1 Announce Type: cross Abstract: We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis.
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Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis
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TransNSNet: A Transformer-Enhanced Multi-Scale Adversarial Framework for CT-to-PET Image Synthesis
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arXiv Machine Learning
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Training-Free Adversarial Robustness in Computational MRI
arXiv:2501. 01908v4 Announce Type: replace-cross Abstract: Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data.
arXiv AI
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Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
The paper introduces CM-RED, a fast MRI reconstruction method that combines a pretrained consistency model with the regularization by denoising framework. By integrating controlled noise injection into accelerated proximal gradient updates, CM-RED achieves high‑quality reconstructions on fastMRI knee and brain datasets with only four network function evaluations. It consistently outperforms existing diffusion‑ and consistency‑based approaches in quantitative metrics, visual fidelity, and robustness to hyperparameter changes.