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C-NMC 2019 Dataset (ISBI 2019 Challenge Data)

Data Description
The dataset contains images of "normal cells" and "malignant/cancer cells". This dataset can be used to train classification models for Acute Lymphoblastic Leukemia (ALL) diagnosis. Detailed description and usage guidelines of data are available at TCIA link below: new

This dataset was used for ISBI-2019 challenge organized by SBILab. The test set predictions of this dataset can be evaluated at challenge leaderboard.
Access Data Here
The dataset is publicly available at The Cancer Imaging Archive (TCIA). Please visit this link to download the dataset.
Citations & Data Usage Policy
Please acknowledge both this data set and TCIA in publications by including the following citations in your work:
Data Citation
  1. Anubha Gupta and Ritu Gupta (2019). C-NMC 2019 Dataset: ALL Challenge dataset of ISBI 2019 [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.dc64i46r
Publication Citation
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
TCIA Citation
  1. 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

MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma Cancer

Data Description
The dataset contains Microscopic images of Multiple Myeloma. The images were captured from bone marrow aspirate slides of patients diagnosed with multiple myeloma as per the standard guidelines. Detailed description and usage guidelines of data are available at TCIA link below: new
Access Data Here
The dataset is publicly available at The Cancer Imaging Archive (TCIA). Please visit this link to download the dataset.
Citations & Data Usage Policy
Please acknowledge both this data set and TCIA in publications by including the following citations in your work:
Data Citation
  1. Ritu Gupta and Anubha Gupta (2019). MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.pnn6aypl
Publication Citation
  1. Anubha Gupta, Pramit Mallick, Ojaswa Sharma, Ritu Gupta, and Rahul Duggal, "PCSeg: Color Model Driven Probabilistic Multiphase Level Set based Tool for Plasma Cell Segmentation in Multiple Myeloma", Accepted, PLOSOne Journal, 2018.
  2. 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.
  3. 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.
TCIA Citation
  1. 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

SN-AM Dataset: White Blood cancer dataset of B-ALL and MM for stain normalization

Data Description
This is a White Blood cancer dataset of B-lineage Acute Lymphoid Leukemia (B-ALL) and Multiple Myeloma (MM) for stain normalization. Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with B-ALL and MM as per the standard guidelines. Detailed description and usage guidelines of data are available at TCIA link below: new
Access Data Here
The dataset is publicly available at The Cancer Imaging Archive (TCIA). Please visit this link to download the dataset.
Citations & Data Usage Policy
Please acknowledge both this data set and TCIA in publications by including the following citations in your work:
Data Citation
  1. Anubha Gupta and Ritu Gupta (2019). SN-AM Dataset: White Blood Cancer Dataset of B-ALL and MM for Stain Normalization [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.of2w8lxr
Publication Citation
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
TCIA Citation
  1. 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