  Medical image analysis frequently encounters data scarcity challenges.
Transfer learning has been effective in addressing this issue while conserving
computational resources. The recent advent of foundational models like the
DINOv2, which uses the vision transformer architecture, has opened new
opportunities in the field and gathered significant interest. However, DINOv2's
performance on clinical data still needs to be verified. In this paper, we
performed a glioma grading task using three clinical modalities of brain MRI
data. We compared the performance of various pre-trained deep learning models,
including those based on ImageNet and DINOv2, in a transfer learning context.
Our focus was on understanding the impact of the freezing mechanism on
performance. We also validated our findings on three other types of public
datasets: chest radiography, fundus radiography, and dermoscopy. Our findings
indicate that in our clinical dataset, DINOv2's performance was not as strong
as ImageNet-based pre-trained models, whereas in public datasets, DINOv2
generally outperformed other models, especially when using the frozen
mechanism. Similar performance was observed with various sizes of DINOv2 models
across different tasks. In summary, DINOv2 is viable for medical image
classification tasks, particularly with data resembling natural images.
However, its effectiveness may vary with data that significantly differs from
natural images such as MRI. In addition, employing smaller versions of the
model can be adequate for medical task, offering resource-saving benefits. Our
codes are available at https://github.com/GuanghuiFU/medical_DINOv2_eval.
