CMIRA: an analyzer for chest x-rays disease by MedCLIP

With the development of Computer-Aided Diagnosis (CAD) systems and Deep Learning (DL) technologies, significant advancements have been made in the automatic analysis of medical images, particularly chest X-rays (CXR). However, the scarcity of annotated medical image data poses challenges to the performance and generalization ability of machine learning algorithms. Recent studies have investigated this issue by utilizing Visual-Language (VL) models such as MedCLIP, which leverage image-text pairs to reduce data acquisition costs and enhance training efficiency. In this paper, we focus on CLIP, a type of VL model which is used for medical image analysis. We have implemented an automatic lung diagnostic system based on MedCLIP: ChexPert-MedCLIP Integrated Radiography Analyzer (CMIRA). This system utilizes advanced DL algorithms and robust understanding capabilities of text-image correlations to achieve rapid and accurate analysis of chest X-ray images. The application of this system aims to provide clinicians with more reliable diagnostic support tools, promoting early detection and treatment of diseases.