Can I read Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models on EtoBox?

Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models by Yunxiang Li; Hua-Chieh Shao; Xiao Liang; Liyuan Chen; Ruiqi Li; Steve Jiang; Jing Wang; You Zhang is a Medicine article available to read on EtoBox.

What is Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models about?

Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequencyguided diffusion model (FGDM) that employs frequencydomain filters to guide the diffusion model for structurepreserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from th

Who reads Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models?

Author

Yunxiang Li; Hua-Chieh Shao; Xiao Liang; Liyuan Chen; Ruiqi Li; Steve Jiang; Jing Wang; You Zhang

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2024

Language

EN

Field

Medicine (Health Sciences)