PEMMA adapts a CT-only transformer segmentation model to CT+PET with LoRA/DoRA, reaching early-fusion-level Dice while training only 0.5-8% of parameters, and extends the same adapters to prognosis.
Touchstone benchmark: Are we on the right way for evaluating ai algorithms for medical segmentation? Advances in Neural Information Processing Systems 37, 15184–15201
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Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
PEMMA adapts a CT-only transformer segmentation model to CT+PET with LoRA/DoRA, reaching early-fusion-level Dice while training only 0.5-8% of parameters, and extends the same adapters to prognosis.