Deep learning based medical volumetric segmentation methods either train the
model from scratch or follow the standard "pre-training then finetuning"
paradigm. Although finetuning a well pre-trained model on downstream tasks can
harness its representation power, the standard full finetuning is costly in
terms of computation and memory footprint. In this paper, we present the first
study on parameter-efficient transfer learning for medical volumetric
segmentation and propose a novel framework named Med-Tuning based on
intra-stage feature enhancement and inter-stage feature interaction. Given a
large-scale pre-trained model on 2D natural images, our method can exploit both
the multi-scale spatial feature representations and temporal correlations along
image slices, which are crucial for accurate medical volumetric segmentation.
Extensive experiments on three benchmark datasets (including CT and MRI) show
that our method can achieve better results than previous state-of-the-art
parameter-efficient transfer learning methods and full finetuning for the
segmentation task, with much less tuned parameter costs. Compared to full
finetuning, our method reduces the finetuned model parameters by up to 4x, with
even better segmentation performance