  Foundation models pre-trained on large-scale data have been widely witnessed
to achieve success in various natural imaging downstream tasks.
Parameter-efficient fine-tuning (PEFT) methods aim to adapt foundation models
to new domains by updating only a small portion of parameters in order to
reduce computational overhead. However, the effectiveness of these PEFT
methods, especially in cross-domain few-shot scenarios, e.g., medical image
analysis, has not been fully explored. In this work, we facilitate the study of
the performance of PEFT when adapting foundation models to medical image
classification tasks. Furthermore, to alleviate the limitations of prompt
introducing ways and approximation capabilities on Transformer architectures of
mainstream prompt tuning methods, we propose the Embedded Prompt Tuning (EPT)
method by embedding prompt tokens into the expanded channels. We also find that
there are anomalies in the feature space distribution of foundation models
during pre-training process, and prompt tuning can help mitigate this negative
impact. To explain this phenomenon, we also introduce a novel perspective to
understand prompt tuning: Prompt tuning is a distribution calibrator. And we
support it by analyzing patch-wise scaling and feature separation operations
contained in EPT. Our experiments show that EPT outperforms several
state-of-the-art fine-tuning methods by a significant margin on few-shot
medical image classification tasks, and completes the fine-tuning process
within highly competitive time, indicating EPT is an effective PEFT method. The
source code is available at github.com/zuwenqiang/EPT.
