The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The importance of stain normalization in colorectal tissue classification with convolutional networks
Open accessAbstract
The development of reliable imaging biomarkers for the analysis of colorectal cancer (CRC) in hematoxylin and eosin (H&E) stained histopathology images requires an accurate and reproducible classification of the main tissue components in the image. In this paper, we propose a system for CRC tissue classification based on convolutional networks (ConvNets). We investigate the importance of stain normalization in tissue classification of CRC tissue samples in H&E-stained images. Furthermore, we report the performance of ConvNets on a cohort of rectal cancer samples and on an independent publicly available dataset of colorectal H&E images.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A method for normalizing histology slides for quantitative analysis
Macenko, M., Niethammer, M., Marron, J. S., Borland, D., Woosley, J. T., Guan, X., Schmitt, C., & Thomas, N. E. (2009) · 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro · doi:10.1109/isbi.2009.5193250 · Cited by 1,482
Cited by
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749
Tellez, D., Litjens, G., Bándi, P., Bulten, W., Bokhorst, J.-M., Ciompi, F., & van der Laak, J. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101544 · Cited by 649
“…(2) detecting the presence of tumor metastases in breast lymph node tissue (B´andi et al. (2019)); (3) detecting the presence of epithelial cells in prostate tissue (Bulten et al. (2019)); and (4) distinguishing among 9 tissue classes in colorectal cancer (CRC) tissue (Ciompi et al. (2017)).”
BackgroundMethodInfluentialSelf-citationDeep learning in microscopy image analysis: a survey
Xing, F., Xie, Y., Su, H., Liu, F., & Yang, L. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2766168 · Cited by 460
Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology
Ke, J., Shen, Y., Liang, X., & Shen, D. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87237-3_55 · Cited by 28
Metadata
- Authors
- Ciompi, F., Geessink, O., Bejnordi, B. E., de Souza, G. S., Baidoshvili, A., Litjens, G., van Ginneken, B., Nagtegaal, I., & van der Laak, J.
- Venue
- 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
- Publisher
- IEEE
- Published
- 2017-04
- Pages
- 160-163
- OpenAlex
- W2594258169
- Cited by
- 184
- References
- 2
- FWCI
- 10.16
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ciompi, F., Geessink, O., Bejnordi, B. E., de Souza, G. S., Baidoshvili, A., Litjens, G., van Ginneken, B., Nagtegaal, I., & van der Laak, J. (2017). The importance of stain normalization in colorectal tissue classification with convolutional networks. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). https://doi.org/10.1109/isbi.2017.7950492
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