Author Correction: A modular cGAN classification framework: Application to colorectal tumor detection
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The original article can be found online at 10.1038/s41598-019-55257-w.
Correction to: Scientific Reports10.1038/s41598-019-55257-w, published online 12 December 2019
This Article contains errors in the Reference list. References 7, 10, 13, 16, 18, 19, 20, 21, 25, 27, 28, 30, 33, 35, 36 and 37 were incorrectly given as:
- 7. Niazi, M. K. K. et al. In Medical Imaging 2018: Digital Pathology. 105810 H (International Society for Optics and Photonics) (2018).
- 10. Niazi, M. K. K. et al. In Medical Imaging: Digital Pathology. 86760I (International Society for Optics and Photonics) (2013).
- 13. Japkowicz, N. In Proc. of the Int'l Conf. on Artificial Intelligence (2000).
- 16. Qin, Z., Zhang, C., Wang, T. & Zhang, S. In International Conference on Advanced Data Mining and Applications. 1–11 (Springer) (2010).
- 18. Shaban, M. T., Baur, C., Navab, N. & Albarqouni, S. J. a. p. a. StainGAN: Stain Style Transfer for Digital Histological Images (2018).
- 19. Bayramoglu, N., Kaakinen, M., Eklund, L. & Heikkilä, J. In ICCV Workshops. 64–71 (2017).
- 20. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818–2826 (2016).
- 21. Liu, Y. et al. Detecting Cancer Metastases on Gigapixel Pathology Images (2017).
- 25. Kohl, M., Walz, C., Ludwig, F., Braunewell, S. & Baust, M. In International Conference Image Analysis and Recognition. 903–913 (Springer) (2018).
- 27. Tavolara, T. E. et al. Colorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E. Vol. 10956 MI (SPIE, 2019).
- 28. Niazi, M. K. K. et al. Generalization of tumor identification algorithms. Vol. 10956 MI (SPIE, 2019).
- 30. Wang, Z. J. h. e. u. c. z. w. r. s. The SSIM index for image quality assessment (2003).
- 33. Goodfellow, I. et al. In Advances in neural information processing systems. 2672–2680 (2016).
- 35. Isola, P., Zhu, J., Zhou, T. & Efros, A. A. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5967–5976 (2017).
- 36. Ronneberger, O. et al. U-Net Convolutional Networks for Biomedical Image Segmentation (2015).
- 37. Deng, J. et al. In Computer Vision and Pattern Recognition. CVPR 2009. IEEE Conference on. 248–255 (Ieee) (2009).
The correct references are listed below as refs. [1]–[16].
[
References
1
Niazi, M. K. K. et al. Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology. Medical Imaging 2018: Digital Pathology Vol. 10581 105810 H (International Society for Optics and Photonics, 2018).
2
Niazi, M. K. K. et al. Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study. Medical Imaging 2013: Digital Pathology. 86760I (International Society for Optics and Photonics, 2013).
3
Japkowicz, N. The class imbalance problem: Significance and strategies. Proc. of the Int'l Conf. on Artificial Intelligence (2000).
4
Qin, Z., Zhang, C., Wang, T. & Zhang, S. Cost sensitive classification in data mining. International Conference on Advanced Data Mining and Applications. 1–11 (Springer, 2010).
5
Shaban, M. T., Baur, C., Navab, N. & Albarqouni, S. StainGAN: Stain Style Transfer for Digital Histological Images. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI, 2019).
6
Bayramoglu, N., Kaakinen, M., Eklund, L. & Heikkilä, J. Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks. ICCV Workshops. 64–71 (2017).
7
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. 2818–2826 (2016).
8
Liu, Y. et al. Detecting Cancer Metastases on Gigapixel Pathology Images. arXiv preprint arXiv:1703.02442. (2017).
9
Kohl, M., Walz, C., Ludwig, F., Braunewell, S. & Baust, M. Assessment of Breast Cancer Histology Using Densely Connected Convolutional Networks. International Conference Image Analysis and Recognition. 903–913 (Springer, 2018).
Tavolara, T. E. et al. Colorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E. Medical Imaging 2019: Digital Pathology. Vol. 10957 MI. (SPIE, 2019).
Niazi, M. K. K. et al. Generalization of tumor identification algorithms. Medical Imaging 2019: Digital Pathology. Vol. 10956 MI (SPIE, 2019).
Wang, Z. The SSIM index for image quality assessment. (2003).
Goodfellow, I. et al. Generative adversarial nets. Advances in neural information processing systems. 2672–2680 (2014).
Isola, P., Zhu, J., Zhou, T. & Efros, A. A. Image-to-Image Translation with Conditional Adversarial Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5967–5976 (2017).
Ronneberger, O. et al. U-Net Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical image computing and computer-assisted intervention, 234–241 (2015).
Deng, J. et al. Imagenet: A large-scale hierarchical image database. Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 248–255 (Ieee, 2009).
]
By Thomas E. Tavolara; M. Khalid Khan Niazi; Vidya Arole; Wei Chen; Wendy Frankel and Metin N. Gurcan
Reported by Author; Author; Author; Author; Author; Author
