Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation

• URL: http://arxiv.org/abs/2505.19746v1
• Date: Mon, 26 May 2025 09:26:36 GMT
• Title: Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
• Authors: Jakov Samardžija, Donik Vršnak, Sven Lončarić,
• Abstract summary: StyleGAN was trained on available 3R biopsy patches and subsequently used to generate 10,000 realistic synthetic images.<n>These were combined with real 0R samples, that is samples without rejection, in various configurations to train ResNet-18 classifiers for binary rejection classification.<n>Results demonstrate that synthetic data improves classification performance, particularly when used in combination with real samples.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. Three classifier variants were evaluated: one trained on real 0R and synthetic 3R images, another using both synthetic and additional real samples, and a third trained solely on real data. All models were tested on an independent set of real biopsy images. Results demonstrate that synthetic data improves classification performance, particularly when used in combination with real samples. The highest-performing model, which used both real and synthetic images, achieved strong precision and recall for both classes. These findings underscore the value of hybrid training strategies and highlight the potential of GAN-based data augmentation in biomedical image analysis, especially in domains constrained by limited annotated datasets.

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