Inspired by recent findings that generative diffusion models learn
semantically meaningful representations, we use them to discover the intrinsic
hierarchical structure in biomedical 3D images using unsupervised segmentation.
We show that features of diffusion models from different stages of a
U-Net-based ladder-like architecture capture different hierarchy levels in 3D
biomedical images. We design three losses to train a predictive unsupervised
segmentation network that encourages the decomposition of 3D volumes into
meaningful nested subvolumes that represent a hierarchy. First, we pretrain 3D
diffusion models and use the consistency of their features across subvolumes.
Second, we use the visual consistency between subvolumes. Third, we use the
invariance to photometric augmentations as a regularizer. Our models achieve
better performance than prior unsupervised structure discovery approaches on
challenging biologically-inspired synthetic datasets and on a real-world brain
tumor MRI dataset.

本研究使用生成扩散模型来发现生物医学三维图像中所有内在层次结构，并设计了三种损失来训练预测性的无监督分割网络，使其能够将三维体积分解为具有层次结构的有意义的子体积，取得了优于现有无监督结构发现方法的性能，可在具有挑战性的仿真数据集和真实的脑肿瘤 MRI 数据集上使用。