GRAFT-XPCI: Dataset of Synchrotron X-Ray Images for Detection of Acute Cellular Rejection after Heart Transplantation

The efficacy of deep learning in the analysis of biomedical images and its integration of advanced computational techniques into diagnostic workflows has long been studied in many prominent works, contributing to more precise and timely interventions for improved patient outcomes. Synchrotron X-ray tomographic imaging has recently been used for endomyocardial biopsy sample scanning to assess acute cellular rejection after heart transplantation, which greatly increases resolution and clarity of acquired samples compared to other tomographic imaging techniques.The primary objectives of this work are: first, GRAFT-XPCI, a novel classification dataset consisting of synchrotron X-ray tomographic images obtained from heart transplant recipients at varying stages of rejection is proposed. The primary purpose of this dataset is to increase understanding of X-PCI image processing and to enable the training of deep learning models to automate the identification and quantification of subtle alterations in the transplanted heart associated with rejection. Secondly, evaluation protocols are proposed that ensure a fair comparison of different methods on the proposed dataset. Thirdly, a comparison of various existing deep learning models for early rejection detection was conducted. Finally, a set of pre-trained ViT-based foundation models that can be used for further research in the field are provided.