AAI_2025_Capstone_Chronicles_Combined

15

demonstrating

full

comprehension

and

CT Scans Using Vision Transformers. Applied Biosciences , 4 (1), 11. https://doi.org/10.3390/applbiosci4010011 He, Z., Jamel, L., Huang, D., Jiang, G., Shaikh, Z. A., Aljohani Khan, Md. A., & Mousavirad, S. J. (2025). Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning. Scientific Reports , 15 (1), 33323. https://doi.org/10.1038/s41598-025-18899 7 Hiraman, A., Viriri, S., & Gwetu, M. (2024). Lung tumor segmentation: A review of the state of the art. Frontiers in Computer Science , 6 , 1423693. https://doi.org/10.3389/fcomp.2024.142369 3 Hsiao, C.-H., Lin, P.-C., Chung, L.-A., Lin, F. Y.-S., Yang, F.-J., Yang, S.-Y., Wu, C.-H., Huang, Y., & Sun, T.-L. (2022). A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images. Computer Methods and Programs in Biomedicine , 221 , 106854. https://doi.org/10.1016/j.cmpb.2022.10685 4 Hwang, E. J., Goo, J. M., Kim, J., Park, S. J., Ahn, S., Park, C. M., & Shin, Y.-G. (2017). Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases. European Radiology , 27 (8), 3257–3265. https://doi.org/10.1007/s00330-016-4713-8 Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for

ownership of all submitted work. Works Cited Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2019). Data From NSCLC-Radiomics (Version 4) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0 M9REI Braghetto, A., Marturano, F., Paiusco, M., Baiesi, M., & Bettinelli, A. (2022). Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset. Scientific Reports , 12 (1), 14132. https://doi.org/10.1038/s41598-022-18085 z Choe, J., Lee, S. M., Do, K.-H., Lee, G., Lee, J.-G., Lee, S. M., & Seo, J. B. (2019). Deep Learning–based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. Radiology , 292 (2), 365–373. https://doi.org/10.1148/radiol.2019181960 Das, I. J., Compton, J. J., Bajaj, A., & Johnstone, P. A. (2021). Intra- and inter-physician variability in target volume delineation in radiation therapy. Journal of Radiation Research , rrab080. https://doi.org/10.1093/jrr/rrab080 Gayap, H. T., & Akhloufi, M. A. (2025). SALM: A Unified Model for 2D and 3D Region of Interest Segmentation in Lung

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