Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular
both with clinicians and researchers as they are capable of providing unique insights into …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

Medical institutions often revoke data access due to the privacy concern of patients.
Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased …

Introduction High-grade gliomas cause cognitive impairment in those who suffer from them.
However, there is a lack of precise data describing the cognitive deficit that occurs in this …

We present a dataset of magnetic resonance imaging (MRI) data (T1, diffusion, BOLD)
acquired in 25 brain tumor patients before the tumor resection surgery, and six months after …

This paper proposes the Adversarial Denoising Diffusion Model (ADDM). Diffusion models
excel at generating high-quality samples, outperforming other generative models. These …

This book presents a comprehensive review of convex optimization-based compressed
sensing magnetic resonance image reconstruction algorithms. Compressed sensing MRI …

Brain tumors are characterised by infiltration along the white matter tracts, posing significant
challenges to precise treatment. Mounting evidence shows that an infiltrative tumor can …

Brain and other nervous system cancer ranks as the tenth most common cause of death.
According to estimates, primary cancerous brain and central nervous system tumors will be …

Generative models, especially diffusion models, have gained traction in image generation
for their high‐quality image synthesis, surpassing generative adversarial networks (GANs) …