Prompt tuning for parameter-efficient medical image segmentation.

Neural networks pre-trained on a self-supervision scheme have become the standard when operating in data rich environments with scarce annotations. As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e.g. for a new set of classes in the case of semantic segmentation, is of increasing importance. In this work, we propose and investigate several contributions to achieve a parameter-efficient but effective adaptation for semantic segmentation on two medical imaging datasets. Relying on the recently popularized prompt tuning approach, we provide a prompt-able UNETR (PUNETR) architecture, that is frozen after pre-training, but adaptable throughout the network by class-dependent learnable prompt tokens. We pre-train this architecture with a dedicated dense self-supervision scheme based on assignments to online generated prototypes (contrastive prototype assignment, CPA) of a student teacher combination. Concurrently, an additional segmentation loss is applied for a subset of classes during pre-training, further increasing the effectiveness of leveraged prompts in the fine-tuning phase. We demonstrate that the resulting method is able to attenuate the gap between fully fine-tuned and parameter-efficiently adapted models on CT imaging datasets. To this end, the difference between fully fine-tuned and prompt-tuned variants amounts to 7.81 pp for the TCIA/BTCV dataset as well as 5.37 and 6.57 pp for subsets of the TotalSegmentator dataset in the mean Dice Similarity Coefficient (DSC, in %) while only adjusting prompt tokens, corresponding to 0.51% of the pre-trained backbone model with 24.4M frozen parameters. The code for this work is available on https://github.com/marcdcfischer/PUNETR.