Metastatic Tissue Classification - PatchCamelyon
- kaggle.com
Updated Apr 20, 2020
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EmailClick to copy linkLink copiedCiteLarxel (2020). Metastatic Tissue Classification - PatchCamelyon [Dataset]. https://www.kaggle.com/andrewmvd/metastatic-tissue-classification-patchcamelyon/codeCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedApr 20, 2020AuthorsLarxelLicensehttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
DescriptionPreview Images
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F793761%2F5d374f25880b39aa2ebfa5fe757a4179%2Fpcam.jpg?generation=1587369639125272&alt=media" alt="Image Sample of PCAM"> Example images from PCam. Green boxes indicate tumor tissue in center region, which dictates a positive label.
About This Data
The PatchCamelyon benchmark (PCAM) consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue.
Fundamental machine learning advancements are predominantly evaluated on straight-forward natural-image classification datasets and medical imaging is becoming one of the major applications of ML and thus deserves a spot on the list of go-to ML datasets. Both to challenge future work, and to steer developments into directions that are beneficial for this domain.
How To Cite this Dataset
If you use this dataset in your research, please credit the authors.
Original Articles
[1] B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962
[2] Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585
Splash Image
Photo by Nephron on Wikimedia Commons
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FacebookTwitterEmailMetastatic Tissue Classification - PatchCamelyon
327.680 color images with binary labels for normal or tumor tissue present
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Preview Images
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F793761%2F5d374f25880b39aa2ebfa5fe757a4179%2Fpcam.jpg?generation=1587369639125272&alt=media" alt="Image Sample of PCAM">
Example images from PCam. Green boxes indicate tumor tissue in center region, which dictates a positive label.
About This Data
The PatchCamelyon benchmark (PCAM) consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue.
Fundamental machine learning advancements are predominantly evaluated on straight-forward natural-image classification datasets and medical imaging is becoming one of the major applications of ML and thus deserves a spot on the list of go-to ML datasets. Both to challenge future work, and to steer developments into directions that are beneficial for this domain.
How To Cite this Dataset
If you use this dataset in your research, please credit the authors.
Original Articles
[1] B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling. "Rotation Equivariant CNNs for Digital Pathology". arXiv:1806.03962
[2] Ehteshami Bejnordi et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA: The Journal of the American Medical Association, 318(22), 2199–2210. doi:jama.2017.14585
Splash Image
Photo by Nephron on Wikimedia Commons