用压缩空间扩散模型提升脑影像无监督学习效率与效果
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
- 在低维隐空间进行扩散建模，显著降低计算开销
- 诊断准确率90%（AUC），年龄预测误差仅4.1年
- 生成图像逼真且可编辑，适合长时序医学影像分析
本研究提出潜空间扩散自编码器（LDAE），一种基于扩散的新型编码器-解码器框架，用于高效且有意义的医学影像无监督学习。以阿尔茨海默病（AD）为例，利用ADNI数据库的脑部MRI数据进行验证。与传统在图像空间运行的扩散自编码器不同，LDAE在压缩的隐空间中执行扩散过程，提升了计算效率，使3D医学影像表征学习成为可能。实验验证两个假设：(i) LDAE能有效捕捉与AD和衰老相关的有意义语义表征；(ii) 实现高质量图像生成与重建，同时具备高效性。结果表明：(i) 线性探测评估显示，对AD诊断的ROC-AUC达90%，准确率84%；年龄预测的平均绝对误差（MAE）为4.1年，均方根误差（RMSE）为5.2年；(ii) 学习到的语义表征支持属性操控，生成解剖结构合理的修改；(iii) 语义插值实验显示，对缺失扫描的重建效果优异，6个月时间缺口下SSIM达0.969（MSE: 0.0019），24个月缺口下仍保持稳健性能（SSIM > 0.93，MSE < 0.004），表明其可捕捉时间演变趋势；(iv) 相比传统方法，LDAE推理吞吐量提升20倍，同时改善重建质量。这些发现表明LDAE是可扩展医学影像应用的有力框架，具有作为医疗影像分析基础模型的潜力。代码已开源。
原文摘要 · Abstract (English)
This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised learning in medical imaging, focusing on Alzheimer disease (AD) using brain MR from the ADNI database as a case study. Unlike conventional diffusion autoencoders operating in image space, LDAE applies the diffusion process in a compressed latent representation, improving computational efficiency and making 3D medical imaging representation learning tractable. To validate the proposed approach, we explore two key hypotheses: (i) LDAE effectively captures meaningful semantic representations on 3D brain MR associated with AD and ageing, and (ii) LDAE achieves high-quality image generation and reconstruction while being computationally efficient. Experimental results support both hypotheses: (i) linear-probe evaluations demonstrate promising diagnostic performance for AD (ROC-AUC: 90%, ACC: 84%) and age prediction (MAE: 4.1 years, RMSE: 5.2 years); (ii) the learned semantic representations enable attribute manipulation, yielding anatomically plausible modifications; (iii) semantic interpolation experiments show strong reconstruction of missing scans, with SSIM of 0.969 (MSE: 0.0019) for a 6-month gap. Even for longer gaps (24 months), the model maintains robust performance (SSIM > 0.93, MSE < 0.004), indicating an ability to capture temporal progression trends; (iv) compared to conventional diffusion autoencoders, LDAE significantly increases inference throughput (20x faster) while also enhancing reconstruction quality. These findings position LDAE as a promising framework for scalable medical imaging applications, with the potential to serve as a foundation model for medical image analysis. Code available at https://github.com/GabrieleLozupone/LDAE
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。