Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to standardize image appearance and improve the consistency of tissue representation. StyleGAN was trained on available 3R biopsy patches and subsequently used to generate 10,000 realistic synthetic images. These were combined with real 0R samples, that is samples without rejection, in various configurations to train ResNet-18 classifiers for binary rejection classification.
View on arXiv@article{samardžija2025_2505.19746,
title={ Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation },
author={ Jakov Samardžija and Donik Vršnak and Sven Lončarić },
journal={arXiv preprint arXiv:2505.19746},
year={ 2025 }
}