  The integration of deep learning systems into healthcare 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 is an open-source foundation model pre-trained with self-supervised
learning on 142 million curated natural images that exhibits promising
capabilities across various vision tasks. Nevertheless, a critical question
remains unanswered regarding DINOv2's adaptability to radiological imaging, and
whether its features are sufficiently general to benefit radiology image
analysis. Therefore, this study comprehensively evaluates DINOv2 for radiology,
conducting over 100 experiments across diverse modalities (X-ray, CT, and MRI).
To measure the effectiveness and generalizability of DINOv2's feature
representations, we analyze the model across medical image analysis tasks
including disease classification and organ segmentation on both 2D and 3D
images, and under different settings like kNN, few-shot learning,
linear-probing, end-to-end fine-tuning, and parameter-efficient fine-tuning.
Comparative analyses with established supervised, self-supervised, and
weakly-supervised models reveal DINOv2's superior performance and cross-task
generalizability. 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. Our code is available at
https://github.com/MohammedSB/DINOv2ForRadiology
