MeDUET：面向3D医学图像合成与分析的解耦统一预训练
MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis
AI总结：
提出MeDUET框架，在变分自编码器潜在空间中通过解耦学习域不变内容因子和域特定风格因子，统一3D医学图像合成与分析任务，提升合成质量、收敛速度和域泛化能力。
摘要：
自监督学习（SSL）和扩散模型分别推动了高维3D视觉数据的表示学习和生成建模，但它们通常作为独立的范式发展。在多源异质性下，由于解剖内容必须保留用于分析，而采集相关的风格在不同中心间变化并影响合成，它们的统一仍然具有挑战性。在本文中，我们提出了MeDUET，一个在变分自编码器潜在空间中的3D医学图像解耦统一预训练框架。MeDUET将统一预训练表述为一个经验因子可识别性问题，旨在学习用于解剖的域不变内容因子和用于外观的域特定风格因子。为了改善因子分离，MeDUET首先使用带有标准对抗域正则化的令牌去混合来建立基本的内容-风格特化，并进一步引入混合因子令牌蒸馏和交换不变四元组对比来减少混合区域的因子泄漏，并以因子不变性和可区分性组织因子空间。利用这些学习到的因子，MeDUET有效迁移到合成和分析任务，在合成中实现更高的保真度、更快的收敛和更好的可控性，同时在多样化的数据集、任务和模态上达到竞争性或更优的域泛化和标签效率。总体而言，MeDUET表明多源异质性可以作为有用的监督，解耦为统一3D医学图像合成和分析提供了有效接口。我们的代码可在以下网址获取：https://this https URL。
英文摘要：
Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimensional 3D visual data, yet they are often developed as separate paradigms. Their unification remains challenging under multi-source heterogeneity, as anatomical content must be preserved for analysis while acquisition-related style varies across centers and affects synthesis. In this paper, we propose MeDUET, a 3D Medical image Disentangled UnifiEd PreTraining framework in the variational autoencoder latent space. MeDUET formulates unified pretraining as an empirical factor identifiability problem, aiming to learn domain-invariant content factors for anatomy and domain-specific style factors for appearance. To improve factor separation, MeDUET first uses token demixing with a standard adversarial domain regularizer to establish basic content-style specialization, and further introduces Mixed Factor Token Distillation and Swap-invariance Quadruplet Contrast to reduce mixed-region factor leakage and organize factor spaces with factor-wise invariance and discriminability. With these learned factors, MeDUET transfers effectively to both synthesis and analysis, yielding higher fidelity, faster convergence, and better controllability for synthesis, while achieving competitive or superior domain generalization and label efficiency on diverse datasets, tasks, and modalities. Overall, MeDUET shows that multi-source heterogeneity can serve as useful supervision, with disentanglement providing an effective interface for unifying 3D medical image synthesis and analysis. Our code is available at https://github.com/JK-Liu7/MeDUET.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
