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  • C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019

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Summary


Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

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Detailed Description

10.44

Excerpt

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. In this paper, we propose a deep learning framework for classifying immature leukemic blasts and normal cells. The proposed model combines the Discrete Cosine Transform (DCT) domain features extracted via CNN with the Optical Density (OD) space features to build a robust classifier. Elaborate experiments have been conducted to validate the proposed LeukoNet classifier.

Localtab Group
Localtab
activetrue
titleData Access
Data TypeDownload all or Query/Filter
Images (.bmp, 10.44 GB)

Image Removed Image Removed

Supplemental Data (csv, pdf)

Image Removed Image Removed

Localtab
titleDetailed Description

Image Statistics

Modalities

Pathology

Number of Patients

118

Number of Studies

118

Number of Images

15,114

Images Size (GB)

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:

15
  • 17, ALL (cancer): 9, Normal: 8

    Total cell images: 2586

, ALL(cancer): 1761, Normal: 825 Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Add any special restrictions in here.

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 help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Info
titleData Citation

DOI goes here. Create using pubhub with information from Collection Approval form

Info
titlePublication Citation

Additional Publications using this dataset:


  • Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy,
“GCTI
  • "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
    • " 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.



    Localtab Group



    Localtab
    activetrue
    titleData Access

    Data Access

    Click the  Download button to browse and download the data from Box.


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


    Click the Versions tab for more info about data releases.




    Localtab
    titleDetailed Description

    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




    Localtab
    titleCitations & Data Usage Policy

    Citations & Data Usage Policy

    Public collection license

    Info
    titleData 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"




    Info
    titlePublication 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.




    Info
    titleTCIA 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.




    Localtab
    titleVersions

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


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

    Image ModifiedImage Removed

    (Requires NBIA Data Retriever.)

    Other (cvs, pdf)

    Image Removed