APA (7th ed.) Citation

Siviengphanom, S., Brennan, P. C., Lewis, S. J., Trieu, P. D., & Gandomkar, Z. (2025). A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult. Journal of Imaging Informatics in Medicine, 38(3), 1904. https://doi.org/10.1007/s10278-024-01291-8

Chicago Style (17th ed.) Citation

Siviengphanom, Somphone, Patrick C. Brennan, Sarah J. Lewis, Phuong Dung Trieu, and Ziba Gandomkar. "A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult." Journal of Imaging Informatics in Medicine 38, no. 3 (2025): 1904. https://doi.org/10.1007/s10278-024-01291-8.

MLA (9th ed.) Citation

Siviengphanom, Somphone, et al. "A Machine Learning Model Based on Global Mammographic Radiomic Features Can Predict Which Normal Mammographic Cases Radiology Trainees Find Most Difficult." Journal of Imaging Informatics in Medicine, vol. 38, no. 3, 2025, p. 1904, https://doi.org/10.1007/s10278-024-01291-8.