The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks
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Cites
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Self-citationA few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements
Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.044 · Cited by 670
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629
Cited by
Artificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095
“Shin et al. (2018) use a conditional GAN to generate synthetic MR images given a lesion mask and a brain segmentation mask.”
Medical image segmentation using deep learning: A survey
Wang, R., Lei, T., Cui, R., Zhang, B., Meng, H., & Nandi, A. K. (2022) · IET Image Processing · doi:10.1049/ipr2.12419 · Cited by 892
Deep semantic segmentation of natural and medical images: a review
Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 870
Generative adversarial network: An overview of theory and applications
Aggarwal, A., Mittal, M., & Battineni, G. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2020.100004 · Cited by 783
Metadata
- Authors
- Shin, H.-C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., & Michalski, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 1-11
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2884065486
- Cited by
- 638
- References
- 6
- FWCI
- 42.29
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. https://app.cochat.ai/papers/10.1007/978-3-030-00536-8_1
- The paper
- Shin, H.-C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C. G., Senjem, M. L., Gunter, J. L., Andriole, K. P., & Michalski, M. (2018). Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-00536-8_1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
