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About this paper
Mobina Mansoori, Sajjad Shahabodini, Farnoush Bayatmakou, Jamshid Abouei, Konstantinos N. Plataniotis, Arash Mohammadi
Researchers from Concordia University and University of Toronto systematically evaluated state-of-the-art natural domain foundation models for medical image classification across four diverse datasets. The study determined that models like AIMv2, DINOv2, and SAM2 consistently achieved high performance, with full fine-tuning of the backbone demonstrating superior outcomes compared to frozen configurations.
Key insights
- Progress in natural domain foundation models directly correlates with improved performance in specialized medical image classification tasks.
- Optimal performance in medical applications is achieved when the entire foundation model, including its backbone, is fine-tuned (unfrozen).
- A linear classification head typically performs best when the backbone is unfrozen, while a multi-layer attention head is more effective for frozen backbone scenarios.
- Newer models like AIMv2, DINOv2, and SAM2 demonstrate consistently high classification accuracy while also requiring comparatively less training time.
Summary by alphaXiv.
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