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<!DOCTYPE html>
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<meta name="description" content="This study introduces a novel self-supervised pre-training approach using diffusion models for landmark detection in X-ray images, significantly outperforming existing methods across three datasets with minimal annotated data (1-50 images).">
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<meta name="keywords" content="Landmark detection, Diffusion models, DDPM, Self-supervised Learning, Few-shot Learning, X-rays">
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<title>Diffusion models for Landmark detection</title>
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<h1 class="title is-1 publication-title" style="font-size: 40px;">
Self-supervised pre-training with diffusion model <br>for few-shot landmark
detection in x-ray images</h1>
<div class="is-size-5 publication-authors">
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<a href="https://scholar.google.com/citations?user=qGS6cv4AAAAJ=en" target="_blank">Roberto Di Via</a>,</span>
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<a href="https://scholar.google.com/citations?user=riK7DscAAAAJ&hl=en" target="_blank">Francesca Odone</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=-boYCXcAAAAJ&hl=en" target="_blank">Vito Paolo Pastore</a>
</span>
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<span class="author-block">MaLGa Center, DIBRIS, University of Genoa<br>Winter Conference on Applications of Computer Vision (WACV) 2025</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
<b>Deep neural networks</b> have been extensively applied in the medical domain for various tasks, <b>including</b> image classification, segmentation, and <b>landmark detection</b>. However, their application is often
<b>hindered by data scarcity</b>, both in terms of available annotations and images. This study introduces
a novel application of denoising diffusion probabilistic models (DDPMs) to landmark detection
task, specifically addressing the challenge of limited annotated data in x-ray imaging. Our key
innovation lies in leveraging DDPMs for self-supervised pre-training in landmark detection, a
previously unexplored approach in this domain. <b>This method enables accurate landmark detection
with minimal annotated training data</b> (as few as 50 images), surpassing both ImageNet supervised
pre-training and traditional self-supervised techniques across three popular x-ray benchmark datasets.
To our knowledge, <b>this work represents the first application of diffusion models for self-supervised
learning in landmark detection</b>, which may offer a valuable pre-training approach in few-shot regimes,
for mitigating data scarcity
</p>
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<h2 class="title is-3">Introduction</h2>
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<p>
This paper introduces a novel application of denoising diffusion probabilistic models (DDPMs) for anatomical landmark detection in X-ray images, specifically <b>addressing the challenge of limited annotated data</b>. The key innovation lies in leveraging <b>DDPMs for self-supervised pre-training in landmark detection</b>, a previously unexplored approach in this domain. The method enables accurate landmark detection with minimal annotated training data (as few as 1-50 images), significantly <b>outperforming both ImageNet supervised pre-training and traditional self-supervised techniques</b> across three popular X-ray benchmark datasets (Chest, Cephalometric, and Hand). A comprehensive evaluation against state-of-the-art alternatives, <b>including YOLO</b>, demonstrates the approach's effectiveness even when pre-trained on one in-domain dataset and fine-tuned on smaller, distinct datasets.
</p>
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<h2 class="title is-3">Few-shot Landmark Detection Results</h2>
<div class="content has-text-justified">
<p>
The paper evaluates the effectiveness of DDPM self-supervised pre-training for landmark detection by <b>benchmarking it against supervised ImageNet pre-training, self-supervised state-of-the-art methods (MoCoV3, SimCLRV2, and DINO), and the YOLO framework</b> across three X-ray datasets: Chest, Cephalometric, and Hand. The proposed approach consistently outperforms both ImageNet and alternative SSL methods across all datasets and training image quantities, with particularly <b>impressive results</b> in low-data regimes. For instance, <b>with just one labeled sample</b> in the Chest dataset, DDPM achieves a Mean Radial Error (MRE) of 14.99px compared to ImageNet's 143.67px, representing an 89.6% improvement. Similar significant performance gains are observed in the Cephalometric dataset (15.71mm vs 86.71mm MRE) and Hand dataset (28.75mm vs 79.32mm MRE). When compared to YOLO, a state-of-the-art universal anatomical landmark detection model that uses mixed dataset training, DDPM achieves competitive or superior results using just one labeled sample. <b>These results demonstrate the method's effectiveness in few-shot learning scenarios, which are common in medical imaging where annotated data is scarce.</b>
</p>
</div>
</div>
</div>
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<h3 class="title is-4">Chest Few-shot Results</h3>
<img src="static/images/chest_label_efficient.png" alt="Chest few-shot results" style="width:100%">
</div>
</div>
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<h3 class="title is-4">Cephalometric Few-shot Results</h3>
<img src="static/images/cephalo_label_efficient.png" alt="Cephalometric few-shot results" style="width:100%">
</div>
</div>
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<h3 class="title is-4">Hand Few-shot Results</h3>
<img src="static/images/hand_label_efficient.png" alt="Hand few-shot results" style="width:100%">
</div>
</div>
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<h3 class="title is-4">Comparison with the state-of-the-art YOLO framework</h3>
<img src="static/images/sota_results.PNG" alt="Comparison with YOLO framework" style="width:100%">
</div>
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<h2 class="title">BibTeX Citation</h2>
<pre><code>
@article{DiVia2024,
author = {Di Via, R. and Odone, F. and Pastore, V. P.},
title = {Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images},
year = {2024},
journal = {arXiv},
volume = {2407.18125},
url = {https://arxiv.org/abs/2407.18125},
}
</code></pre>
<h2 class="title">APA Citation</h2>
<pre><code>
Di Via, R., Odone, F., & Pastore, V. P. (2024). Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images. ArXiv. https://arxiv.org/abs/2407.18125
</code></pre>
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