用低参数量方法高效微调3D扩散模型生成脑部MRI图像。
Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks
- 基于张量网络压缩3D卷积核，实现极低参数微调。
- 在三个脑MRI数据集上达到最优结构相似性，仅需原模型0.3%参数。
- 适合资源受限下高精度医学图像生成任务的科研与临床应用。
针对基于3D U-Net的去噪扩散概率模型（DDPM）在磁共振成像（MRI）图像生成中的参数高效微调（PEFT）挑战，本文提出专为3D卷积骨干设计的张量体操作器（TenVOO）。通过张量网络建模，将3D卷积核表示为低维张量，以少量参数有效捕捉微调过程中的复杂空间依赖关系。我们在三个下游脑部MRI数据集（ADNI、PPMI、BraTS2021）上评估，对在英国生物银行59,830例T1加权脑部MRI扫描预训练的DDPM进行微调。结果表明，TenVOO在多尺度结构相似性指数（MS-SSIM）上达到当前最优性能，同时仅需原模型0.3%的可训练参数。
原文摘要 · Abstract (English)
We address the challenge of parameter-efficient fine-tuning (PEFT) for three-dimensional (3D) U-Net-based denoising diffusion probabilistic models (DDPMs) in magnetic resonance imaging (MRI) image generation. Despite its practical significance, research on parameter-efficient representations of 3D convolution operations remains limited. To bridge this gap, we propose Tensor Volumetric Operator (TenVOO), a novel PEFT method specifically designed for fine-tuning DDPMs with 3D convolutional backbones. Leveraging tensor network modeling, TenVOO represents 3D convolution kernels with lower-dimensional tensors, effectively capturing complex spatial dependencies during fine-tuning with few parameters. We evaluate TenVOO on three downstream brain MRI datasets-ADNI, PPMI, and BraTS2021-by fine-tuning a DDPM pretrained on 59,830 T1-weighted brain MRI scans from the UK Biobank. Our results demonstrate that TenVOO achieves state-of-the-art performance in multi-scale structural similarity index measure (MS-SSIM), outperforming existing approaches in capturing spatial dependencies while requiring only 0.3% of the trainable parameters of the original model. Our code is available at: https://github.com/xiaovhua/tenvoo
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