Beware of diffusion models for synthesizing medical images—a comparison with GANs in terms of memorizing brain MRI and chest x-ray images
Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by generative adversarial networks (GANs), diffusion models have shown impressive results using various evaluation metrics. However, commonly used met...
Saved in:
Main Authors: | Muhammad Usman Akbar, Wuhao Wang, Anders Eklund |
---|---|
Format: | Article |
Language: | English |
Published: |
IOP Publishing
2025-01-01
|
Series: | Machine Learning: Science and Technology |
Subjects: | |
Online Access: | https://doi.org/10.1088/2632-2153/ad9a3a |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging
by: Luca Vellini, et al.
Published: (2025-01-01) -
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region
by: Paritt Wongtrakool, et al.
Published: (2025-02-01) -
LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection
by: Hari Mohan Rai, et al.
Published: (2025-01-01) -
Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer
by: Yao Huo, et al.
Published: (2025-01-01) -
Investigating the effect of loss functions on single-image GAN performance
by: Eyyup YİLDİZ, et al.
Published: (2024-12-01)