Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and
privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an …

Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical
images, driving advances in medical image analysis, disease diagnosis, and treatment …

Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to
localize tumors and observe their contrast kinetics, which is essential for cancer …

Large-scale generative models have demonstrated impressive capabilities in producing
visually compelling images, with increasing applications in medical imaging. However, they …

Accurately translating medical images across different modalities (eg, CT to MRI) has
numerous downstream clinical and machine learning applications. While several methods …

Determining whether two sets of images belong to the same or different domain is a crucial
task in modern medical image analysis and deep learning, where domain shift is a common …

In the realm of medical image analysis, self-supervised learning (SSL) techniques have
emerged to alleviate labeling demands, while still facing the challenge of training data …

Single-source domain generalization (SDG) aims to learn a model from a single source
domain that can generalize well on unseen target domains. This is an important task in …

Automatic medical segmentation is crucial for assisting doctors in identifying disease
regions effectively. As a state-of-the-art (SOTA) approach, generative AI models, particularly …

This paper presents a method for virtual contrast enhancement in breast MRI, offering a
promising non-invasive alternative to traditional contrast agent-based DCE-MRI acquisition …