Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Publisher
Springer Science and Business Media LLC
Reference16 articles.
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).