Contrastive Learning Based Stain Normalization Across Multiple Tumor in Histopathology
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The pathology tasks were focused on histopathological classification, with the majority focused on colorectal cancer classification.
“The tasks were focused on histopathological classification, where the majority focused on colorectal cancer classification68,84–87.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [68]
With the exception of Ke et al.'s work, all studies that combined contrastive with generative strategies utilized an autoencoder as their generative model.
“With the exception of Ke et al.’s work68, which uses a CycleGAN for histopathology slide stain normalization, all studies utilized an autoencoder as their generative model when combined with contrastive strategies.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [68]
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Self-citation
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
“With the exception of Ke et al. ’ s work 68 , which uses a CycleGAN for histopathology slide stain normalization, all studies utilized an autoencoder as their generative model when combined with contrastive strategies.”
Method
- Authors
- Ke, J., Shen, Y., Liang, X., & Shen, D.
- Affiliations
- Shanghai Jiao Tong University (CN)
- ShanghaiTech University (CN)
- Corresponding author
- Shen, Yiqing
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 571-580
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3202902586
- Cited by
- 28
- References
- 24
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 6 · 2023: 6 · 2022: 5 · 2021: 1
- FWCI
- 2.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Health Sciences › Medicine › Pharmacology › Biological Stains and Phytochemicals
- Keywords
- contrastive learning, color normalization, H&E staining, histopathological images, generative adversarial networks, self-supervised learning, tumor histology, histology image classification, mode collapse
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