Parameter-Efficient pretrained-CT-to-MRI Transfer for Rectal Cancer Segmentation: Performance-Calibration Trade-offs

TL;DR - SWIFT transfers a CT-pretrained Swin V2 encoder to rectal-cancer MRI segmentation using parameter-efficient fine-tuning. Its variants substantially reduce model and trainable parameter counts while exposing trade-offs among tumor detection, boundary accuracy, and uncertainty calibration.

• Decoder compression cut parameters by 70.1% (72.8M to 21.8M) and improved tumor detection from 89.9% to 93.9%, with a slight surface DSC decrease from 0.62 to 0.61.
• LoRA used only 14.6% of SWIFTe’s trainable parameters while retaining similar segmentation performance.
• A four-member LoRA-decoder ensemble produced the best post-temperature-scaling calibration, though its 0.217 expected calibration error showed substantial residual miscalibration.
• Tumor-aware augmentation improved detection but reduced boundary agreement, highlighting a clinically relevant detection–segmentation trade-off.