Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

Recently diffusion models (DM) have been applied in magnetic resonance imaging (MRI)
super-resolution (SR) reconstruction exhibiting impressive performance especially with …

Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal
technique, can provide a more comprehensive view of the lesions, aiding physicians in …

Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs.
Deep learning approaches have been used to enhance dMRI and predict diffusion …

Radiation therapy serves as an effective and standard method for cancer treatment.
Excellent radiation therapy plans always rely on high-quality dose distribution maps …

In multi-modal magnetic resonance imaging (MRI), the tasks of imputing or reconstructing
the target modality share a common obstacle: the accurate modeling of fine-grained inter …