Extracting training data from diffusion models
Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …
significant attention due to their ability to generate high-quality synthetic images. In this work …
Generative AI for brain image computing and brain network computing: a review
Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …
offer a non-invasive approach to map** the structure and function of the brain …
A survey on generative diffusion models
Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …
capturing and generalizing patterns within data, we have entered the epoch of all …
Generating synthetic data for medical imaging
Artificial intelligence (AI) models for medical imaging tasks, such as classification or
segmentation, require large and diverse datasets of images. However, due to privacy and …
segmentation, require large and diverse datasets of images. However, due to privacy and …
Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images
Despite continued advancement in recent years, deep neural networks still rely on large
amounts of training data to avoid overfitting. However, labeled training data for real-world …
amounts of training data to avoid overfitting. However, labeled training data for real-world …
Segment anything model (sam) for radiation oncology
In this study, we evaluate the performance of the Segment Anything Model (SAM) model in
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …
clinical radiotherapy. We collected real clinical cases from four regions at the Mayo Clinic …
[HTML][HTML] A survey of emerging applications of diffusion probabilistic models in mri
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …
a gradual sampling process to synthesize data, have gained increasing research interest …
Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions
Image data augmentation constitutes a critical methodology in modern computer vision
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
tasks, since it can facilitate towards enhancing the diversity and quality of training datasets; …
Probing the limits and capabilities of diffusion models for the anatomic editing of digital twins
Numerical simulations of cardiovascular device deployment within digital twins of patient-
specific anatomy can expedite and de-risk the device design process. Nonetheless, the …
specific anatomy can expedite and de-risk the device design process. Nonetheless, the …
Synthetically enhanced: unveiling synthetic data's potential in medical imaging research
Summary Background Chest X-rays (CXR) are essential for diagnosing a variety of
conditions, but when used on new populations, model generalizability issues limit their …
conditions, but when used on new populations, model generalizability issues limit their …