Medical radiography segmentation, and specifically dental radiography, is
highly limited by the cost of labeling which requires specific expertise and
labor-intensive annotations. In this work, we propose a straightforward
pre-training method for semantic segmentation leveraging Denoising Diffusion
Probabilistic Models (DDPM), which have shown impressive results for generative
modeling. Our straightforward approach achieves remarkable performance in terms
of label efficiency and does not require architectural modifications between
pre-training and downstream tasks. We propose to first pre-train a Unet by
exploiting the DDPM training objective, and then fine-tune the resulting model
on a segmentation task. Our experimental results on the segmentation of dental
radiographs demonstrate that the proposed method is competitive with
state-of-the-art pre-training methods.

医学放射学分割，尤其是牙科放射学，受标注成本的限制，这需要专门的专业知识和劳动密集型注释。本文提出了一种基于去噪扩散概率模型（DDPM）的简单的预训练方法，用于语义分割，该方法在生成模型方面显示出令人印象深刻的结果。我们的简单方法在标签效率方面取得了显著的性能，并且在预训练和下游任务之间不需要架构修改。我们首先利用 DDPM 的训练目标对 Unet 进行预训练，然后在分割任务上对结果模型进行微调。我们对牙科放射图像分割的实验结果表明，所提出的方法与现有的最先进的预训练方法相竞争。