Skip to main content

C-NMC-2019

The Cancer Imaging Archive

C-NMC 2019 | C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019

DOI: 10.7937/tcia.2019.dc64i46r | Data Citation Required | 1.3k Views | 29 Citations | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Blood and Bone Human 118 Histopathology, Other Leukemia 10.44GB Public, Complete 2019/03/26

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

Data Access

Version 1: Updated 2019/03/26

Title Data Type Format Access Points Subjects Studies Series Images License
Slide Images Histopathology PDF, BMP, and CSV
Download requires IBM-Aspera-Connect plugin
 118 118 15,135 CC BY 3.0
README Other PDF CC BY 3.0
Related Datasets
No related Analysis Results found: Submit your proposal! No related Collections found
Legend: Analysis Results| Collections

Additional Resources for this Dataset

This dataset was also used for our IEEE ISBI 2019 conference challenge: Classification of Normal vs Malignant Cells in B-ALL White Blood Cancer Microscopic Images. The challenge is available here: https://biomedicalimaging.org/2019/challenges/

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Mourya, S., Kant, S., Kumar, P., Gupta, A., & Gupta, R. (2019). ALL Challenge dataset of ISBI 2019 (C-NMC 2019) (Version 1) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.dc64i46r

Detailed Description

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

Related Publications

Publications by the Dataset Authors

The authors recommended the following as the best source of additional information about this dataset:

Publication Citation

Gehlot, S., Gupta, A., & Gupta, R. (2020). SDCT-AuxNetθ : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. In Medical Image Analysis (Vol. 61, p. 101661). Elsevier BV. https://doi.org/10.1016/j.media.2020.101661

Publication Citation

Gupta, A., Duggal, R., Gehlot, S., Gupta, R., Mangal, A., Kumar, L., Thakkar, N., & Satpathy, D. (2020). GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. In Medical Image Analysis (Vol. 65, p. 101788). Elsevier BV. https://doi.org/10.1016/j.media.2020.101788

Publication Citation

Gupta, R., Mallick, P., Duggal, R., Gupta, A., & Sharma, O. (2017). 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. In Clinical Lymphoma Myeloma and Leukemia (Vol. 17, Issue 1, p. e99). Elsevier BV. https://doi.org/10.1016/j.clml.2017.03.178

Publication Citation

Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., & Ahuja, C. (2016). Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing. ICVGIP ’16: Indian Conference on Computer Vision, Graphics and Image Processing. ACM. https://doi.org/10.1145/3009977.3010043

Publication Citation

Duggal, R., Gupta, A., & Gupta, R. 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.

Publication Citation

Duggal, R., Gupta, A., Gupta, R., & Mallick, P. (2017). SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging. In Lecture Notes in Computer Science (pp. 435–443). Springer International Publishing. https://doi.org/10.1007/978-3-319-66179-7_50

Publication Citation

Gupta, R., Gehlot, S., & Gupta, A. (2022). C-NMC: B-lineage acute lymphoblastic leukaemia: A blood cancer dataset. Medical Engineering & Physics, 103. doi: https://doi.org/10.1016/j.medengphy.2022.103793

Publication Citation

Goswami, S., Mehta, S., Sahrawat, D., Gupta, A., & Gupta, R. (2020). Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer (Version 2). ICLR workshop on Affordable AI in healthcare, 2020. arXiv preprint https://doi.org/10.48550/arXiv.2003.03295

Publication Citation

Gehlot, S., Gupta, A., & Gupta, R. (2021). A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis. Medical image analysis, 72, 102099. doi:https://doi.org/10.1016/j.media.2021.102099

No other publications were recommended by dataset authors.

Publication Citation

Gehlot, S., Gupta, A., & Gupta, R. (2020). SDCT-AuxNetθ : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. In Medical Image Analysis (Vol. 61, p. 101661). Elsevier BV. https://doi.org/10.1016/j.media.2020.101661

Publication Citation

Gupta, A., Duggal, R., Gehlot, S., Gupta, R., Mangal, A., Kumar, L., Thakkar, N., & Satpathy, D. (2020). GCTI-SN: Geometry-inspired chemical and tissue invariant stain normalization of microscopic medical images. In Medical Image Analysis (Vol. 65, p. 101788). Elsevier BV. https://doi.org/10.1016/j.media.2020.101788

Publication Citation

Gupta, R., Mallick, P., Duggal, R., Gupta, A., & Sharma, O. (2017). 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. In Clinical Lymphoma Myeloma and Leukemia (Vol. 17, Issue 1, p. e99). Elsevier BV. https://doi.org/10.1016/j.clml.2017.03.178

Publication Citation

Duggal, R., Gupta, A., Gupta, R., Wadhwa, M., & Ahuja, C. (2016). Overlapping cell nuclei segmentation in microscopic images using deep belief networks. In Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing. ICVGIP ’16: Indian Conference on Computer Vision, Graphics and Image Processing. ACM. https://doi.org/10.1145/3009977.3010043

Publication Citation

Duggal, R., Gupta, A., & Gupta, R. 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.

Publication Citation

Duggal, R., Gupta, A., Gupta, R., & Mallick, P. (2017). SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging. In Lecture Notes in Computer Science (pp. 435–443). Springer International Publishing. https://doi.org/10.1007/978-3-319-66179-7_50

Publication Citation

Gupta, R., Gehlot, S., & Gupta, A. (2022). C-NMC: B-lineage acute lymphoblastic leukaemia: A blood cancer dataset. Medical Engineering & Physics, 103. doi: https://doi.org/10.1016/j.medengphy.2022.103793

Publication Citation

Goswami, S., Mehta, S., Sahrawat, D., Gupta, A., & Gupta, R. (2020). Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer (Version 2). ICLR workshop on Affordable AI in healthcare, 2020. arXiv preprint https://doi.org/10.48550/arXiv.2003.03295

Publication Citation

Gehlot, S., Gupta, A., & Gupta, R. (2021). A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis. Medical image analysis, 72, 102099. doi:https://doi.org/10.1016/j.media.2021.102099

Research Community Publications

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Other Publications Using this Data

TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.