Abstract: The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generative models, ...

AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...

Two related, Oracle-backed projects published opposing policies on open-source contributions created with generative AI: the ...

Subcortical ischemic vascular disease (SIVD), driven by cerebral small vessel disease, is commonly characterized by white matter hyperintensities and multiple lacunar infarcts, and a substantial ...

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

Aims This study aims to investigate whether denoising diffusion probabilistic models (DDPMs) could generate realistic retinal images, and if they could be used to improve the performance of a deep ...

Abstract: Cross-domain few-shot learning (CDFSL) has demonstrated remarkable new class recognition capabilities in hyperspectral image classification (HSIC) tasks. However, existing domain adaptation ...

Latent Diffusion Autoencoders (LDAE) is a novel unsupervised framework for representation learning in 3D medical imaging. The method compresses 3D MRI scans using an AutoencoderKL, then applies a ...

Neurodegenerative diseases such as Alzheimer's disease (AD) or frontotemporal lobar degeneration (FTLD) involve specific loss of brain volume, detectable in vivo using T1-weighted MRI scans.

The task of image fusion for optical images and SAR images is to integrate valuable information from source images. Recently, owing to powerful generation, diffusion models, e.g., diffusion denoising ...