Confident Pseudo-labeled Diffusion Augmentation for Canine Cardiomegaly Detection

Shiman Zhang, Lakshmikar Polamreddy, Youshan Zhang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 269-278

Canine cardiomegaly marked by an enlarged heart poses serious health risks if undetected requiring accurate diagnostic methods. Current detection models often rely on small poorly annotated datasets and struggle to generalize across diverse imaging conditions limiting their real-world applicability. To address these issues we propose a Confident Pseudo-labeled Diffusion Augmentation (CDA) model for identifying canine cardiomegaly. Our approach tackles the challenge of limited high-quality training data by generating synthetic X-ray images using diffusion models and labeling Vertebral Heart Score key points to expand the dataset. We also employ a pseudo-labeling strategy with Monte Carlo Dropout to select high-confidence labels refine the synthetic dataset and improve accuracy. Iteratively incorporating these labels enhances the model's performance overcoming the limitations of existing approaches. Experimental results show that the CDA model outperforms traditional methods achieving state-of-the-art accuracy in canine cardiomegaly detection. The code implementation is available at https://github.com/Shira7z/CDA.