EAFP-Med: An Efficient Adaptive Feature Processing Module Based on
  Prompts for Medical Image Detection
Xiang Li Long Lan Husam Lahza Shaowu Yang Shuihua Wang Wenjing Yang Hengzhu Liu Yudong Zhang
In the face of rapid advances in medical imaging, cross-domain adaptive
medical image detection is challenging due to the differences in lesion
representations across various medical imaging 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 can efficiently extract lesion features of different scales
from a diverse range of medical images based on prompts while being flexible
and not limited by specific imaging techniques. Furthermore, it serves as a
feature preprocessing module that can be 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 demonstrate that EAFP-Med ST achieves the best performance on all three
datasets (chest X-ray images, cranial magnetic resonance imaging images, and
skin images). EAFP-Med can efficiently extract lesion features from various
medical images based on prompts, enhancing the model's performance. This holds
significant potential for improving medical image analysis and diagnosis.