The integration of deep learning systems into the medical domain has been
hindered by the resource-intensive process of data annotation and the inability
of these systems to generalize to different data distributions. Foundation
models, which are models pre-trained on large datasets, have emerged as a
solution to reduce reliance on annotated data and enhance model
generalizability and robustness. DINOv2, an open-source foundation model
pre-trained with self-supervised learning on 142 million curated natural
images, excels in extracting general-purpose visual representations, exhibiting
promising capabilities across various vision tasks. Nevertheless, a critical
question remains unanswered regarding DINOv2's adaptability to radiological
imaging, and the clarity on whether its features are sufficiently general to
benefit radiology image analysis is yet to be established. Therefore, this
study comprehensively evaluates DINOv2 for radiology, conducting over 100
experiments across diverse modalities (X-ray, CT, and MRI). Tasks include
disease classification and organ segmentation on both 2D and 3D images,
evaluated under different settings like kNN, few-shot learning, linear-probing,
end-to-end fine-tuning, and parameter-efficient fine-tuning, to measure the
effectiveness and generalizability of the DINOv2 feature embeddings.
Comparative analyses with established medical image analysis models, U-Net and
TransUnet for segmentation, and CNN and ViT models pre-trained via supervised,
weakly supervised, and self-supervised learning for classification, reveal
DINOv2's superior performance in segmentation tasks and competitive results in
disease classification. The findings contribute insights to potential avenues
for optimizing pre-training strategies for medical imaging and enhancing the
broader understanding of DINOv2's role in bridging the gap between natural and
radiological image analysis.

本研究全面评估了基于自学习的 DINOv2 模型在放射学领域的应用，通过超过 100 个实验在不同模态下进行疾病分类和器官分割等任务，发现 DINOv2 在分割任务中表现优越，在疾病分类中具有竞争力的结果。这些发现为优化医学影像的预训练策略和加强 DINOv2 在自然图像与放射图像分析之间的桥梁作用提供了深入的见解。