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

TL;DR

EAFP-Med introduces a prompt-based adaptive feature processing module that enhances lesion detection across diverse medical images, improving performance and flexibility in medical image analysis.

Contribution

The paper presents EAFP-Med, a novel prompt-based feature processing module, and EAFP-Med ST, a disease detection model using Swin Transformer V2, achieving superior results across multiple datasets.

Findings

EAFP-Med ST outperforms nine state-of-the-art methods on three datasets.

EAFP-Med effectively extracts lesion features from diverse medical images.

The method improves medical image detection accuracy and adaptability.

Abstract

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.