EAFP-Med: An efficient adaptive feature processing module based on prompts for medical image detection

The rapid proliferation of medical imaging technologies presents a significant challenge for cross-domain adaptive image detection, as lesion representations can vary dramatically across technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med incorporates a prompt-driven dynamic parameter update mechanism, empowering it to extract cross-domain multi-scale lesion features from medical images of diverse modalities adaptively. This exceptional flexibility liberates it from the constraints of any particular imaging technique, fostering great adaptability. Furthermore, EAFP-Med can also serve as a feature preprocessing module connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 – Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results show that the overall accuracy of EAFP Med ST on chest X-ray, brain magnetic resonance imaging, and skin image datasets is 98.47%, 97.60%, and 99.06%, respectively, superior to all the compared state-of-the-art methods.