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Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.
Train set composition: Total subjects: 73, ALL (cancer): 47, Normal: 26 Total cell images: 10,661, ALL(cancer): 7272, Normal: 3389 Preliminary test set composition: Total subjects: 28, ALL (cancer): 13, Normal: 15 Total cell images: 1867, ALL(cancer): 1219, Normal: 648 Final test set composition: Total subjects: 17, ALL (cancer): 9, Normal: 8 Total cell images: 2586
Challenge is split into 3 separate phases:
Train set composition:
Total subjects: 73, ALL (cancer): 47, Normal: 26
Total cell images: 10,661, ALL(cancer): 7272, Normal: 3389
Preliminary test set composition:
Total subjects: 28, ALL (cancer): 13, Normal: 15
Total cell images: 1867, ALL(cancer): 1219, Normal: 648
Final test set composition:
Total subjects: 17, ALL (cancer): 9, Normal: 8
Total cell images: 2586
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Number of Patients
Number of Studies
Number of Images
|Images Size (GB)||10.44|
Please see the readme for a more detailed description of the dataset: CNMC_readme.pdf
Citations & Data Usage Policy
These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to firstname.lastname@example.org. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
"Gupta, A., & Gupta, R. (2019). ALL Challenge dataset of ISBI 2019 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.dc64i46r"
- Anubha Gupta, Rahul Duggal, Ritu Gupta, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, “GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images,”, under review.
- Ritu Gupta, Pramit Mallick, Rahul Duggal, Anubha Gupta, and Ojaswa Sharma, "Stain Color Normalization and Segmentation of Plasma Cells in Microscopic Images as a Prelude to Development of Computer Assisted Automated Disease Diagnostic Tool in Multiple Myeloma," 16th International Myeloma Workshop (IMW), India, March 2017.
- Rahul Duggal, Anubha Gupta, Ritu Gupta, Manya Wadhwa, and Chirag Ahuja, “Overlapping Cell Nuclei Segmentation in Microscopic Images UsingDeep Belief Networks,” Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), India, December 2016.
- Rahul Duggal, Anubha Gupta, and Ritu Gupta, “Segmentation of overlapping/touching white blood cell nuclei using artificial neural networks,” CME Series on Hemato-Oncopathology, All India Institute of Medical Sciences (AIIMS), New Delhi, India, July 2016.
- Rahul Duggal, Anubha Gupta, Ritu Gupta, and Pramit Mallick, "SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging," In: Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, MICCAI 2017. Lecture Notes in Computer Science, Part III, LNCS 10435, pp. 435–443. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-66179-7_50.
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
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