Author:
Deng Ruining,Li Yanwei,Li Peize,Wang Jiacheng,Remedios Lucas W.,Agzamkhodjaev Saydolimkhon,Asad Zuhayr,Liu Quan,Cui Can,Wang Yaohong,Wang Yihan,Tang Yucheng,Yang Haichun,Huo Yuankai
Publisher
Springer Nature Switzerland
Reference30 articles.
1. Amgad, M., et al.: NuCLS: a scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer. GigaScience 11 (2022)
2. Bankhead, P., et al.: Qupath: open source software for digital pathology image analysis. Sci. Rep. 7(1), 1–7 (2017)
3. Bouteldja, N., et al.: Deep learning-based segmentation and quantification in experimental kidney histopathology. J. Am. Soc. Nephrol. 32(1), 52–68 (2021)
4. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
5. Comaniciu, D., Meer, P.: Cell image segmentation for diagnostic pathology. Advanced algorithmic approaches to medical image segmentation: State-of-the-art applications in cardiology, neurology, mammography and pathology, 541–558 (2002)