ARCHIVES

1. Ben-Cohen, A., Klang, E., Raskin, S. P., Amitai, M. M., Greenspan, H., & Shimon, O. (2019). Deep learning for liver lesion
classification: A comparison between contrast-enhanced CT and MRI. European Radiology, 29(10), 5279–5287.
https://doi.org/10.1007/s00330-019-06047-5
2. Hamm, C. A., Wang, C. J., Savic, L. J., Ferrante, M., Schobert, I., Schlachter, T., ... & Chapiro, J. (2019). Deep learning for liver
tumor diagnosis part I: Development of a convolutional neural network classifier for multi-phasic MRI. European Radiology, 29(7),
3338–3347. https://doi.org/10.1007/s00330-018-5817-7
3. Yasaka, K., Akai, H., Kunimatsu, A., Abe, O., & Kiryu, S. (2018). Deep learning with convolutional neural network for differentiation
of liver masses at dynamic contrast-enhanced CT: A preliminary study. Radiology, 286(3), 887–896.
https://doi.org/10.1148/radiol.2017170706
4. Liu, F., Zhang, X., Peters, T. M., & Zheng, Y. (2020). Deep learning-based classification of primary and secondary liver cancer using
multi- phase CT images. Biomedical Signal Processing and Control, 62, 102074. https://doi.org/10.1016/j.bspc.2020.102074
5. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in
healthcare.Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
6. Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505–515.
https://doi.org/10.1148/rg.2017160130
7. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van der Laak, J. A. (2017). A survey on deep
learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005Adwait Kadu,
Dilip Kumar Sultania, “Design, Fabrication and Implementation of automatic cradle”, 2015, IEEE 11th International conference on
mobile computing, networking and Communications.
8. Christ, P. F., Elshaer, M. E. A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., ... & Menze, B. H. (2017). Automatic liver and tumor
segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. Medical Image Analysis, 46, 1–13.
https://doi.org/10.1016/j.media.2017.03.004
9. Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., ... & Aerts, H. J. (2019). Artificial intelligence in
cancer imaging: Clinical challenges and applications. CA: A Cancer Journal for Clinicians, 69(2), 127–157.
https://doi.org/10.3322/caac.21552
10. Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C. A., ... & Halama, N. (2019). Predicting survival from
colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine, 16(1), e1002730.
https://doi.org/10.1371/journal.pmed.1002730
11. Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2021). Learning modality-invariant latent representation for semi-
supervised liver tumor segmentation. IEEE Transactions on Medical Imaging, 40(1), 66–78.
https://doi.org/10.1109/TMI.2020.3013984
12. Ma, J., Wu, F., Jiang, T., Zhu, J., Kong, D., & Zhang, H. (2021). Fine-grained liver tumor classification with CT images using a deep
network. IEEE Access, 9, 39048–39056. https://doi.org/10.1109/ACCESS.2021.3063606
13. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–
248. https://doi.org/10.1146/annurev-bioeng-071516-044442
14. Liu, S., Xie, Y., Zhang, J., & Xia, Y. (2020). Fusing multi-view information in graph convolutional networks for liver tumor
segmentation. Medical Image Analysis, 60, 101593. https://doi.org/10.1016/j.media.2019.101593