Pre-Training with Diffusion models for Dental Radiography segmentation
This addresses the high labeling costs in medical imaging for dental applications, but it is incremental as it adapts existing diffusion models to a specific domain.
The authors tackled the problem of limited labeled data for dental radiography segmentation by proposing a pre-training method using Denoising Diffusion Probabilistic Models (DDPM), achieving competitive performance with state-of-the-art methods in terms of label efficiency.
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.