基于多模态图像融合的早期蕈样肉芽肿识别

Early Mycosis Fungoides Recognition Based on Multimodal Image Fusion

早期蕈样肉芽肿(Mycosis fungoid,MF)可表现为红斑鳞屑性皮损,很难从银屑病及慢性湿疹等良性炎症性皮肤病中鉴别出来。本文提出了一种基于多模态图像融合的早期蕈样肉芽肿识别方法。该方法基于皮肤镜图像和临床图像,采用ResNet18网络提取单模态图像的特征;设计跨模态的注意力模块,实现两种模态图像的特征融合;并且设计自注意力模块提取融合特征中的关键信息,改善信息冗余,从而提高蕈样肉芽肿智能识别的准确度。实验结果表明,本文所提出的智能诊断模型优于对比算法。将本文模型应用于皮肤科医生的实际临床诊断,通过实验组医生和对照组医生平均诊断准确率的变化证实了本文模型能够有效提升临床诊断水平。

Early mycosis fungoides(MFs)may present as erythematous scaly skin lesions,which are difficult to distinguish from benign inflammatory skin diseases such as psoriasis and chronic eczema.This paper presents a new method based on multimodal image fusion for early mycosis fungoides recognition.The method adopts the ResNet18 network to extract features of single-modality images based on dermoscopic images and clinical images,designs the cross-modal attention module to achieve feature fusion of two modal images,and uses the self-attention module to extract the key information and reduce redundant information in the fusion features,thereby improving the accuracy of intelligent identification of early mycosis fungoides.Experimental results show that the proposed intelligent diagnosis model outperforms the comparison algorithms.At the same time,the proposed intelligent model is applied to the actual clinical diagnosis of dermatologists.Through the changes in the average diagnostic accuracy of the experimental group and the control group,it is confirmed that the proposed intelligent diagnostic model can effectively improve the clinical diagnosis level.