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.

翻译：医学放射影像分割，特别是牙科放射影像分割，因其标注成本高昂且需要专业知识和大量人工标注而受到极大限制。本文提出一种简洁的语义分割预训练方法，该方法利用在生成式建模中表现卓越的去噪扩散概率模型（DDPM）。我们的简洁方法在标签效率方面取得了显著性能，且无需在预训练与下游任务之间修改网络架构。具体而言，我们首先利用DDPM训练目标对UNet进行预训练，然后在下游分割任务中对预训练模型进行微调。在牙科放射影像分割上的实验结果表明，所提方法与当前最先进的预训练方法具有可比性。