Below is a list of my peer-reviewed publications in the fields of digital healthcare, medical imaging, and predictive modeling.
Below is a list of my peer-reviewed publications in the fields of digital healthcare, medical imaging, and predictive modeling.
Original Articles (1st author)
Yoo, S.K., Kim, T.H., Kim, J.S., Ahn, S.S., Kim, E.H., Sung, W., Kim, H. and Yoon, H.I., 2025. Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model. Yonsei Medical Journal, (In Press) (Co-first author)
Yoo, S. K., Kim, K.H., Noh, J.M., Oh, J., Yang, G., Kim, J., Kim, N., Kim, H. and Yoon, H.I., 2024. Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information. Radiotherapy and Oncology, 201, p.110566. (Co-first author) [Link to paper]
Choi, B.S., Yoo, S.K., Moon, J., Chung, S.Y., Oh, J., Baek, S., Kim, Y., Chang, J.S., Kim, H. and Kim, J.S., 2023. Acute coronary event (ACE) prediction following breast radiotherapy by features extracted from 3D CT, dose, and cardiac structures. Medical physics, 50(10), pp.6409-6420. (Co-first author) [Link to paper]
Lee, J., Yoo, S.K., Kim, K., Lee, B.M., Park, V.Y., Kim, J.S. and Kim, Y.B., 2023. Machine learning‑based radiomics models for prediction of locoregional recurrence in patients with breast cancer. Oncology Letters, 26(4), p.422. (Co-first author) [Link to paper]
Yoo, S.K., Kim, H., Choi, B.S., Park, I. and Kim, J.S., 2022. Generation and evaluation of synthetic computed tomography (CT) from cone-beam CT (CBCT) by incorporating feature-driven loss into intensity-based loss functions in deep convolutional neural network. Cancers, 14(18), p.4534. [Link to paper]
Yoo, S.K., Kim, T.H., Chun, J., Choi, B.S., Kim, H., Yang, S., Yoon, H.I. and Kim, J.S., 2022. Deep-learning-based automatic detection and segmentation of brain metastases with small volume for stereotactic ablative radiotherapy. Cancers, 14(10), p.2555. (Co-first author) [Link to paper]
Original Articles (Co-author)
Choi, B., Beltran, C.J., Yoo, S.K., Kwon, N.H., Kim, J.S. and Park, J.C., 2024. The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck. Journal of Personalized Medicine, 14(9), p.979. [Link to paper]
Kim, H., Yoo, S.K., Kim, J.S., Kim, Y.T., Lee, J.W., Kim, C., Hong, C.S., Lee, H., Han, M.C., Kim, D.W. and Kim, S.Y., 2024. Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT. Scientific reports, 14(1), p.8504. [Link to paper]
Kim, H., Yoo, S.K., Kim, D.W., Lee, H., Hong, C.S., Han, M.C. and Kim, J.S., 2022. Metal artifact reduction in kV CT images throughout two-step sequential deep convolutional neural networks by combining multi-modal imaging (MARTIAN). Scientific Reports, 12(1), p.20823. [Link to paper]
Lee, S., Chu, Y.S., Yoo, S.K., Choi, S., Choe, S.J., Koh, S.B., Chung, K.Y., Xing, L., Oh, B. and Yang, S., 2020. Augmented decision‐making for acral lentiginous melanoma detection using deep convolutional neural networks. Journal of the European Academy of Dermatology and Venereology, 34(8), pp.1842-1850. [Link to paper]