Park, Ji Eun

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019) · Korean Journal of Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 388

Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis

Park, S. H., Han, K., Jang, H. Y., Park, J. E., Lee, J.-G., Kim, D. W., & Choi, J. (2023) · Radiology

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 163

A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features

Park, J. E., Kim, H. S., Kim, D., Park, S. Y., Kim, J. Y., Cho, S. J., & Kim, J. H. (2020) · BMC Cancer

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 126