Semi-supervised latent diffusion model for Biliary Atresia class-imbalanced image recognition

Abstract

Biliary Atresia (BA) is a congenital condition affecting the bile ducts in newborn babies worldwide, typically diagnosed by the color of infant stools in clinical practice. Due to the limited availability of pediatricians and the lack of effective diagnostic tools for parents, computer vision is devised as a non-invasive and convenient means for early detection of BA. However, the limited recognition accuracy of BA and the challenge of imbalanced classes in real-world scenarios restrict the generalizability of existing methods, posing a challenge to their decision-making performance. To address those challenges, we introduce a Latent Diffusion Biliary Atresia method (LDBA) to leverage imbalanced data to enhance the recognition of BA. Specifically, we propose a novel pre-trained Biliary Atresia network (PBNet) to predict labels and effectively filter out high-threshold data. Then, a new data augmentation method is proposed to focus attention on the color and anomaly points of images. Subsequently, a new label fusion method and dynamic thresholds strategy are devised to optimize the utilization of generated data by the Latent Diffusion Model (LDM). The entire process is trained in a semi-supervised manner. The experimental results indicate that our proposed LDBA outperforms competing methods with a superior performance of 78.43%. Our method can improve the accuracy and reliability of BA clinical diagnosis.