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In the face of rapid advances in medical imaging, cross-domain adaptivemedical image detection is challenging due to the differences in lesionrepresentations across various medical imaging technologies. To address thisissue, we draw inspiration from large language models to propose EAFP-Med, anefficient adaptive feature processing module based on prompts for medical imagedetection. EAFP-Med can efficiently extract lesion features of different scalesfrom a diverse range of medical images based on prompts while being flexibleand not limited by specific imaging techniques. Furthermore, it serves as afeature preprocessing module that can be connected to any model front-end toenhance the lesion features in input images. Moreover, we propose a noveladaptive disease detection model named EAFP-Med ST, which utilizes the SwinTransformer 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. Experimentalresults demonstrate that EAFP-Med ST achieves the best performance on all threedatasets (chest X-ray images, cranial magnetic resonance imaging images, andskin images). EAFP-Med can efficiently extract lesion features from variousmedical images based on prompts, enhancing the model's performance. This holdssignificant potential for improving medical image analysis and diagnosis.

EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection