nnFoundation: 3D Foundation Models for Radiology
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Abstract
nnFoundation presents complementary convolutional and transformer-based 3D radiological foundation models that outperform prior models across various tasks.
Reality Card
nnFoundation models establish state-of-the-art performance for radiological imaging across 108 tasks, demonstrating that performance is influenced by the interplay of scalable pretraining, complementary architectures, and dataset-aware adaptation.
Trained on 2.1 million CT, MRI, and PET image volumes from 125 datasets, achieving superior performance compared to prior models.
The performance is task-dependent, which may complicate generalization across all tasks.
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