Benchmarking transferability of SSL pretraining to same and different modality segmentation tasks
Methods: Nine SSL methods spanning four pretext-task families were pretrained from scratch using the same 10{,}412 3D CT scans (1.89~M 2D axial slices) covering varied disease sites. The pretrained Swin Transformer encoder from each method was integrated into a SwinUNETR-style segmentation network (Swin encoder with a 3D CNN decoder and skip connections) and fine-tuned on nine public segmentation tasks of varying complexity, including large abdominal organs, head-and-neck structures, and tumors from CT and MRI. Performance was assessed using Dice similarity coefficient (DSC). Fine-tuning convergence speed, transferability across modalities (CT-to-MRI), and feature-reuse patterns between f...
| Authors | Jue Jiang, Harini Veeraraghavan |
|---|---|
| Primary category | cs.CV |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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