Vercauteren, Tom, Mathias Unberath, Nicolas Padoy, and Nassir Navab. "Cai4cai: the rise of contextual artificial intelligence in computer-assisted interventions." Proceedings of the IEEE 108, no. 1 (2019): 198-214.
Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., Fallert, J., Feussner, H., Giannarou, S., Mascagni, P. and Nakawala, H., 2020. Surgical Data Science--from Concepts to Clinical Translation. arXiv preprint arXiv:2011.02284.
Budd, S., Robinson, E.C. and Kainz, B., 2021. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis, p.102062.
Shen, D., Wu, G. and Suk, H.I., 2017. Deep learning in medical image analysis. Annual review of biomedical engineering, 19, pp.221-248.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B. and Sánchez, C.I., 2017. A survey on deep learning in medical image analysis. Medical image analysis, 42, pp.60-88.
Haskins, G., Kruger, U. and Yan, P., 2020. Deep learning in medical image registration: a survey. Machine Vision and Applications, 31(1), pp.1-18.
Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Wang, Z. and Fey, A.M., 2018. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery. International journal of computer assisted radiology and surgery, 13(12), pp.1959-1970.