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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 | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Blood and Bone Human 118 Histopathology 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 PDF CC BY 3.0
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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 this paper as the best source of additional information about this dataset:

  • 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

No publications by dataset authors were found.

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

Research Community Publications

  • 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.

The following publications are recommended by the data submitters that may be useful to researchers utilizing this collection:

  • 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
  • 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
  • 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
  • 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 Using Deep Belief Networks,” Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), India, December 2016.

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

  1. Ciga, O., Xu, T., & Martel, A. L. (2022). Self supervised contrastive learning for digital histopathology. Machine Learning with Applications, 7. doi:https://doi.org/10.1016/j.mlwa.2021.100198
  2. Jawahar, M., H, S., L, J. A., & Gandomi, A. H. (2022). ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification. Comput Biol Med, 148, 105894. doi:https://doi.org/10.1016/j.compbiomed.2022.105894
  3. Mohammed, K. K., Hassanien, A. E., & Afify, H. M. (2023). Refinement of ensemble strategy for acute lymphoblastic leukemia microscopic images using hybrid CNN-GRU-BiLSTM and MSVM classifier. Neural Computing and Applications, 35(23), 17415-17427. doi:https://doi.org/10.1007/s00521-023-08607-9
  4. Rastogi, P., Khanna, K., & Singh, V. (2022). LeuFeatx: Deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear. Comput Biol Med, 142, 105236. doi:https://doi.org/10.1016/j.compbiomed.2022.105236
  5. Rizki Firdaus, M., Ema, U., & Dhani, A. (2023). Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(4), 947-952. doi:https://doi.org/10.29207/resti.v7i4.5182
  6. Talaat, F. M., & Gamel, S. A. (2023). A2M-LEUK: attention-augmented algorithm for blood cancer detection in children. Neural Computing and Applications, 35(24), 18059-18071. doi:https://doi.org/10.1007/s00521-023-08678-8

Other Publications Using this Data

  • 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.

The following publications are recommended by the data submitters that may be useful to researchers utilizing this collection:

  • 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
  • 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
  • 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
  • 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 Using Deep Belief Networks,” Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), India, December 2016.

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

  1. Ciga, O., Xu, T., & Martel, A. L. (2022). Self supervised contrastive learning for digital histopathology. Machine Learning with Applications, 7. doi:https://doi.org/10.1016/j.mlwa.2021.100198
  2. Jawahar, M., H, S., L, J. A., & Gandomi, A. H. (2022). ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification. Comput Biol Med, 148, 105894. doi:https://doi.org/10.1016/j.compbiomed.2022.105894
  3. Mohammed, K. K., Hassanien, A. E., & Afify, H. M. (2023). Refinement of ensemble strategy for acute lymphoblastic leukemia microscopic images using hybrid CNN-GRU-BiLSTM and MSVM classifier. Neural Computing and Applications, 35(23), 17415-17427. doi:https://doi.org/10.1007/s00521-023-08607-9
  4. Rastogi, P., Khanna, K., & Singh, V. (2022). LeuFeatx: Deep learning-based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear. Comput Biol Med, 142, 105236. doi:https://doi.org/10.1016/j.compbiomed.2022.105236
  5. Rizki Firdaus, M., Ema, U., & Dhani, A. (2023). Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(4), 947-952. doi:https://doi.org/10.29207/resti.v7i4.5182
  6. Talaat, F. M., & Gamel, S. A. (2023). A2M-LEUK: attention-augmented algorithm for blood cancer detection in children. Neural Computing and Applications, 35(24), 18059-18071. doi:https://doi.org/10.1007/s00521-023-08678-8