The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Deep Bayesian Active Learning with Image Data
Ghahramani, Z., Gal, Y., & Islam, R. (2017) · PMLR · doi:10.17863/cam.11070 · Cited by 392
Cited by
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
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
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606
On the interpretability of artificial intelligence in radiology: challenges and opportunities
Reyes, M., Meier, R., Pereira, S., Silva, C. A., Dahlweid, F.-M., Tengg-Kobligk, H. V., Summers, R. M., & Wiest, R. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020190043 · Cited by 484
Self-citationImage super-resolution using progressive generative adversarial networks for medical image analysis
Mahapatra, D., Bozorgtabar, B., & Garnavi, R. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.10.005 · Cited by 308
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Metadata
- Authors
- Mahapatra, D., Bozorgtabar, B., Thiran, J.-P., & Reyes, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 580-588
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2808256519
- Cited by
- 166
- References
- 7
- FWCI
- 8.11
- Type
- conference paper
- Language
- en
- Open access
- green
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- The paper
- Mahapatra, D., Bozorgtabar, B., Thiran, J.-P., & Reyes, M. (2018). Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-00934-2_65
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