Reproducibility Dataset for:GAN-based Bone Suppression Using a Combined Loss Function (2026) This record contains the full reproducibility package associated with the accepted publication: Jochymek L., Vašinková M., Doležíl V., Gajdoš P.GAN-based bone suppression using a combined loss function.(2026) The archive includes: Jupyter notebooks implementing GAN, Autoencoder, and U-Net models Configuration settings corresponding to the published experiments Trained model weights for best-performing configurations Dependency specification (requirements.txt) Citation metadata and licensing information Dataset Information: The experiments were conducted using the publicly available JSRT chest radiograph dataset: Japanese Society of Radiological Technology (JSRT)Standard Digital Image Databasehttp://db.jsrt.or.jp/eng.php The original JSRT dataset is not redistributed in this archive due to licensing restrictions.Users must obtain the dataset directly from the official source. Purpose: This reproducibility package ensures transparency, methodological validation, and long-term archival of the experimental configuration reported in the paper. Technical Environment: Python 3.6.8TensorFlow 2.6.2Segmentation Models v1.0.1CUDA 11.4 The package enables full replication of the results reported in Tables 1–8 of the publication. Funding: This work was supported by: Center for Artificial Intelligence and Quantum Computing in System Brain Research (CLARA), Grant No. 101136607 Research Platform for Digital Transformation and Society 5.0, Grant No. CZ.02.01.01/00/23 021/0012599