Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Open accessAbstract
Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by noisy and expensive reference standards established by pathologists, lack of generalization due to staining variation across laboratories, and high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying H&E color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from the cancer genome atlas on the three tasks of the tumor proliferation assessment challenge. We obtained a performance within the top three best methods for most of the tasks of the challenge.
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Many specialized methods have been developed to handle stain variation in the context of digital histopathology.
“Many specialized methods have been developed to handle stain variation in the context of digital histopathology.”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Tellez et al. (2018)
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Virtual Staining for Mitosis Detection in Breast Histopathology
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Stain Standardization Capsule for Application-Driven Histopathological Image Normalization
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Stain Mix-Up: Unsupervised Domain Generalization for Histopathology Images
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- Authors
- Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., Litjens, G., van der Laak, J., & Ciompi, F.
- Affiliations
- Radboud University Medical Center (NL)
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
- Canisius-Wilhelmina Ziekenhuis (NL)
- Jeroen Bosch Ziekenhuis (NL)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2018-09
- Volume
- 37
- Issue
- 9
- Pages
- 2126-2136
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2794616003
- Cited by
- 288
- References
- 27
- Citations by year
- 2026: 16 · 2025: 27 · 2024: 49 · 2023: 41 · 2022: 47 · 2021: 41 · 2020: 39 · 2019: 24 · 2018: 3
- FWCI
- 15.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Radboud Universitair Medisch Centrum
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- mitosis detection, breast cancer, whole slide images, H&E staining, knowledge distillation, data augmentation, image registration, tumor cell proliferation, histopathology, convolutional neural networks
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