Pediatric heart transplantation represents the standard of care for children confronting end-stage heart failure. One of the most common postoperative complications, heart transplant rejection, has been monitored via surveillance endomyocardial biopsies and manual assessment by cardiac pathology experts. However, manual annotations with interobserver and intraobserver variability among cardiovascular pathology experts lead to significant disagreements about the severity of rejection. Artificial intelligence (AI)-enabled computational pathology usually requires large-scale manual annotations of gigapixel whole-slide images (WSIs) for effective model training. To address these challenges, we develop an AI-enabled rare disease detection framework for automating heart transplant rejection detection from WSIs of pediatric patients. Specifically, we conduct dataset cartography with data maps and training dynamics to map and diagnose the augmented samples, exploring the model behavior on individual instances during model training. Extensive experiments on internal and external patient cohorts have demonstrated the feasibility of both tile-level and biopsy-level detection with augmented samples. The proposed data-efficient learning framework may support seamless scalability to real-world rare disease detection without the burden of iterative expert annotations.