Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.

MLA

Cho, Kyungjin, et al. “Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.” Journal of Imaging Informatics in Medicine, vol. 37, no. 4, Aug. 2024, pp. 1375–85. EBSCOhost, https://doi.org/10.1007/s10278-024-01032-x.

APA

Cho, K., Kim, K. D., Jeong, J., Nam, Y., Kim, J., Choi, C., Lee, S., Hong, G.-S., Seo, J. B., & Kim, N. (2024). Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography. Journal of Imaging Informatics in Medicine, 37(4), 1375–1385. https://doi.org/10.1007/s10278-024-01032-x

Chicago

Cho, Kyungjin, Ki Duk Kim, Jiheon Jeong, Yujin Nam, Jeeyoung Kim, Changyong Choi, Soyoung Lee, Gil-Sun Hong, Joon Beom Seo, and Namkug Kim. 2024. “Approximating Intermediate Feature Maps of Self-Supervised Convolution Neural Network to Learn Hard Positive Representations in Chest Radiography.” Journal of Imaging Informatics in Medicine 37 (4): 1375–85. doi:10.1007/s10278-024-01032-x.