The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
Open accessAbstract
Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.
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Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · cited 1×
“For example, a comparison of algorithms competing in the CAMELYON16 challenge to pathologists in clinical practice was recently published [29].”
Self-citationDeep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069
Self-citationDetecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506
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The Future of Digital Health with Federated Learning
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv · doi:10.48550/arxiv.2003.08119 · Cited by 3,495
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Deep learning in histopathology: the path to the clinic
van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902
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Metadata
- Authors
- Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., Manson, Q. F., Stathonikos, N., Baidoshvili, A., van Diest, P., Wauters, C., van Dijk, M., & van der Laak, J.
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-31
- Volume
- 7
- Issue
- 6
- ISSN
- 2047-217X (ISSN-L)
- OpenAlex
- W2805886241
- Cited by
- 418
- References
- 5
- FWCI
- 17.26
- Type
- journal article
- Language
- en
- Open access
- gold
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- Litjens, G., Bandi, P., Ehteshami Bejnordi, B., Geessink, O., Balkenhol, M., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., Manson, Q. F., Stathonikos, N., Baidoshvili, A., van Diest, P., Wauters, C., van Dijk, M., & van der Laak, J. (2018). 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. GigaScience. https://doi.org/10.1093/gigascience/giy065
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