Pre-Training with Diffusion models for Dental Radiography segmentation
- URL: http://arxiv.org/abs/2307.14066v2
- Date: Thu, 27 Jul 2023 10:22:56 GMT
- Title: Pre-Training with Diffusion models for Dental Radiography segmentation
- Authors: J\'er\'emy Rousseau, Christian Alaka, Emma Covili, Hippolyte Mayard,
Laura Misrachi, Willy Au
- Abstract summary: We propose a straightforward pre-training method for semantic segmentation.
Our approach achieves remarkable performance in terms of label efficiency.
Our experimental results on the segmentation of dental radiographs demonstrate that the proposed method is competitive with state-of-the-art pre-training methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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