Pancreatic Anomaly Detection in Abdominal CT Images using Latent Diffusion Models

Early diagnosis and treatment of pancreatic diseases are crucial for improving patient outcomes. Abdominal CT scans provide high-resolution images essential for accurate diagnosis, but analyzing these images is challenging due to the pancreas's complex anatomy and requires expert labor. We propose an anomaly detection algorithm using a latent diffusion model to generate disease-free CT images from abnormal slices. In this study, we used data from 579 patients, including 478 healthy individuals and 101 with pancreatic diseases, training the model on healthy slices only. About 98,000 2D images of 512x512 pixels were used with optimized window-level settings for contrast enhancement. The proposed algorithm achieved an accuracy of 0.837 and an AUC of 0.890. In additional experiments with random slices from 50 normal and 50 abnormal cases, it achieved an accuracy of 0.830 and an AUC of 0.878, demonstrating potential for rapid, effective diagnostic support in clinical settings.