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  • C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019 (C-NMC 2019)

Summary

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.

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

Additional Publications using this dataset:


  • Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788.
  • 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.
  • 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.


Data Access

Data TypeDownload all or Query/FilterLicense
Images (BMP, CSV, PDF, 10.44 GB)

   

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CC BY 3.0

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Detailed Description

Image Statistics


Modalities

Pathology

Number of Participants

118

Number of Studies

118

Number of Images

15,135

Images Size (GB)10.44

Please see the readme for a more detailed description of the dataset: CNMC_readme.pdf

Citations & Data Usage Policy

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

"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"

Publication Citation

  • Shiv Gehlot, Anubha Gupta, and Ritu Gupta, “SDCT-AuxNetθ: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis,” Medical Image Analysis, Elsevier, vol. 61, pp. 1-15, April 2020, DOI: https://doi.org/10.1016/j.media.2020.101661.
  • Shubham Goswami, Suril Mehta, Dhruv Sahrawat, Anubha Gupta and Ritu Gupta, “Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer", ICLR workshop on Affordable AI in healthcare, 2020. arXiv preprint arXiv:2003.03295.

TCIA Citation

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

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Version 1 (Current): Updated 2019/03/26

Data TypeDownload all or Query/Filter
Images (BMP, CSV, PDF, 10.44 GB)


    



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