Fiarka, F., Hakim, A. R., Budiansah, I., Wicaksono, A. A., Jabar, J. M., Peters, S. M., Prive, B. M., Nagarajah, J., Konijnenberg, M. W., Siregar, S., & Hardiansyah, D. (2026). Limitations of mono-exponential individual fitting as a reference model for single-time-point dosimetry in [177Lu]Lu-PSMA-617 therapy. Zeitschrift für Medizinische Physik.

Mudrikah, M. A., Julia, S., Azizah, Z., & Siregar, S. (2026). Quantifying how lesion texture and shape influence deep learning segmentation in breast ultrasound. Biomedical Signal Processing and Control, 125, 110788.

Siregar, S. (2026). Coffee brewing as a context for teaching porous media flow and mass transfer principles. European Journal of Physics, 47(3), 035101.

Ceesay, M. B., Saputro, A. H., & Siregar, S. (2026). Hierarchical tissue-based MRI features with explainable machine learning for Alzheimer's disease classification. Jurnal Ilmu Fisika, 18(1), 93–104.

Siregar, S., Azizah, Z., & Julia, S. (2025). Shape feature analysis for differentiating benign and malignant breast lesions in ultrasound. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Ceesay, M. B., Saputro, A. H., & Siregar, S. (2025). Histogram-based slice-level features for Alzheimer's disease classification using machine learning on MR images. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Azizah, Z., & Siregar, S. (2025). An explainable deep learning pipeline for breast cancer detection using ResNet-50 and Grad-CAM. In Proceedings of the 2025 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM). IEEE.

Siregar, S., Rafianto, A., & Lubis, L. E. (2024). Optimizing Raman spectra pre-processing and classification for prostate cancer detection in tissue specimens. AIP Conference Proceedings, 3210(1).

Siregar, S., Nurhikmat, A., Amdani, R. Z., Hatmi, R. U., Kobarsih, M., Kusumaningrum, A., Karim, M. A., et al. (2024). Estimation of proximate composition in rice using ATR-FTIR spectroscopy and chemometrics. ACS Omega.

Op De Beeck, M., Troein, C., Siregar, S., Gentile, L., Abbondanza, G., Peterson, C., Persson, P., & Tunlid, A. (2020). Regulation of fungal decomposition at the single-cell level. The ISME Journal, 1–10.

Troein, C., Siregar, S., Op De Beeck, M., Peterson, C., Tunlid, A., & Persson, P. (2019). OCTAVVS: A graphical toolbox for high-throughput preprocessing and analysis of vibrational spectroscopy imaging data. bioRxiv.

Siregar, S., & Saijo, Y. (2018). Photoacoustic image denoising using dictionary learning. In Proceedings of the 3rd International Conference on Biomedical Signal and Image Processing (pp. 34–37).

Siregar, S., Oktamuliani, S., & Saijo, Y. (2018). A theoretical model of laser heating carbon nanotubes. Nanomaterials, 8(8), 580.

Siregar, S., Nagaoka, R., Ul Haq, I., & Saijo, Y. (2018). Non-local means denoising in photoacoustic imaging. Japanese Journal of Applied Physics, 57(7S1), 07LB06.

Siregar, S., Nagaoka, R., Ishikawa, K., & Saijo, Y. (2017). Carbon nanotubes as a potential contrast agent for photoacoustic imaging. In Proceedings of Meetings on Acoustics (6ICU), 32(1), 020018.

Ul Haq, I., Nagaoka, R., Siregar, S., & Saijo, Y. (2017). Sparse-representation-based denoising of photoacoustic images. Biomedical Physics & Engineering Express, 3(4), 045014.

Ul Haq, I., Siregar, S., Nagaoka, R., & Saijo, Y. (2017). Vascular bifurcation detection by symmetric analysis of eigenvalues. pp. 30–36.

Saito, R., Nugraha, A. R. T., Hasdeo, E. H., Siregar, S., Guo, H., & Yang, T. (2015). Ultraviolet Raman spectroscopy of graphene and transition-metal dichalcogenides. physica status solidi (b), 252(11), 2363–2374.

Liu, H.-L., Siregar, S., Hasdeo, E. H., Kumamoto, Y., Shen, C.-C., Cheng, C.-C., Li, L.-J., Saito, R., & Kawata, S. (2015). Deep-ultraviolet Raman scattering studies of monolayer graphene thin films. Carbon, 81, 807–813.

Majidi, M. A., Siregar, S., & Rusydi, A. (2014). Theoretical study of optical conductivity of graphene with magnetic and nonmagnetic adatoms. Physical Review B, 90(19), 195442.