用对称分布对齐实现多期增强MRI高效合成
Flip Distribution Alignment VAE for Multi-Phase MRI Synthesis
- 通过双向对称编码分离共享与独立特征
- 参数量减少，推理速度更快，合成质量更优
- 适合需要轻量化与可解释性的医学图像生成任务
分离共享与独立特征对多期对比增强（CE）MRI合成至关重要。现有方法采用参数效率低的深度自编码器生成器，且缺乏可解释的训练策略。本文提出轻量级特征解耦变分自编码器FDA-VAE，将输入与目标图像编码为相对于标准正态分布对称的两个潜在分布，有效分离共享与独立特征。Y型双向训练策略进一步提升了特征分离的可解释性。实验表明，相比现有基于深度自编码器的端到端合成方法，FDA-VAE显著减少模型参数与推理时间，同时有效提升合成质量。源代码已公开于 https://github.com/QianMuXiao/FDA-VAE。
原文摘要 · Abstract (English)
Separating shared and independent features is crucial for multi-phase contrast-enhanced (CE) MRI synthesis. However, existing methods use deep autoencoder generators with low parameter efficiency and lack interpretable training strategies. In this paper, we propose Flip Distribution Alignment Variational Autoencoder (FDA-VAE), a lightweight feature-decoupled VAE model for multi-phase CE MRI synthesis. Our method encodes input and target images into two latent distributions that are symmetric concerning a standard normal distribution, effectively separating shared and independent features. The Y-shaped bidirectional training strategy further enhances the interpretability of feature separation. Experimental results show that compared to existing deep autoencoder-based end-to-end synthesis methods, FDA-VAE significantly reduces model parameters and inference time while effectively improving synthesis quality. The source code is publicly available at https://github.com/QianMuXiao/FDA-VAE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。