Summary
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Acute lymphoblastic leukemia (ALL) constitutes |
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approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells |
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under the microscope is challenging because morphologically the images of the two cells appear similar. |
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title | Data Access |
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title | Detailed Description |
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Detailed Description
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Image Statistics
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Modalities
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Pathology
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Number of Patients
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118
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Number of Studies
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118
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Number of Images
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15,114
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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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Click the Versions tab for more info about data releases.
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:
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17, ALL (cancer): 9, Normal: 8
Total cell images: 2586
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title | Citations & Data Usage Policy |
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Citations & Data Usage Policy
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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:
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DOI goes here. Create using pubhub with information from Collection Approval form |
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Additional Publications using this dataset:
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- Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy,
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- "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images,
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- " 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.
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(Requires NBIA Data Retriever.)
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