Region and Global-Specific PatchCore based Anomaly Detection from Chest X-ray Images

This paper introduces a method aimed at diagnosing the presence or absence of lesions by detecting anomalies in Chest X-ray images.The proposed approach is based on the PatchCore anomaly detection method, which extracts a feature vector containing location information of an image patch from normal image data and calculates the anomaly distance from the normal vector.However, applying PatchCore directly to medical image processing presents challenges due to the possibility of diseases occurring only in specific organs and the presence of image noise unrelated to lesions.In this study, we present an image alignment method that utilizes affine transformation parameter prediction to standardize already captured X-ray images into a specific composition.Additionally, we introduce a region-specific abnormality detection method that requires affine-transformed chest X-ray images.Furthermore, we propose a method to enhance application efficiency and performance through feature map hard masking.The experimental results demonstrate that our proposed approach achieved a maximum AUROC (Area Under the Receiver Operating Characteristic) of 0.774.Compared to a previous study conducted on the same dataset, our method shows a 6.9% higher performance and improved accuracy.