Confident Pseudo-labeled Diffusion Augmentation for Canine Cardiomegaly Detection
This work addresses the need for accurate diagnostic methods in veterinary medicine, though it is incremental as it builds on existing diffusion and pseudo-labeling techniques for a specific domain.
The paper tackled the problem of limited and poorly annotated datasets for canine cardiomegaly detection by proposing a Confident Pseudo-labeled Diffusion Augmentation model, which achieved state-of-the-art accuracy in identifying enlarged hearts in dogs.
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 addresses the challenge of limited high-quality training data by employing diffusion models to generate synthetic X-ray images and annotate Vertebral Heart Score key points, thereby expanding 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.