Summary
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. |
...
Explanation of Dataset
...
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
Additional Publications using this dataset:
- 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.
Localtab Group |
---|
Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data Access |
|
Click the Download button to browse and download the data from Box.
Data Type | Download all or Query/Filter | License |
---|
Slide Images (BMP, CSV, PDF, 10.44 GB) |
Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ4OSIsInBhc3Njb2RlIjoiZTgxZmNjNzc3YjZkNTRiNDlhNTdiZjk4ZGYwY2MwNzhiNDQyZjhiYiIsInBhY2thZ2VfaWQiOiI0ODkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:c_nmc_2019 |
---|
|
|
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | | README (PDF, 75 KB) |
|
|
...
| Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/download/attachments/52758223/CNMC_readme.pdf?api=v2 |
---|
|
|
| |
|
|
...
Click the Versions tab for more info about data releases. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed Description
| |
---|
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 |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage Policy |
|
...
Tcia limited license policy |
---|
|
|
...
Mourya, S., Kant, S., Kumar, P., Gupta, A., & Gupta, R. (2019). ALL Challenge dataset of ISBI 2019 (C-NMC 2019 |
|
|
...
...
Info |
---|
title | Publication Citation |
---|
|
|
|
...
...
...
& 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 |
Info |
---|
| 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 DataThe 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.
|
|
Info |
---|
|
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
- 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. - 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
- 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
- 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
- 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
- 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
- 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
|
Localtab |
---|
| Version 1 (Current): Updated 2019/03/26
Data Type | Download all or Query/Filter |
---|
Images (BMP, CSV, PDF, 10.44 GB) |
|
|
...
| Tcia button generator |
---|
url | https://faspex.cancerimagingarchive.net/aspera/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ4OSIsInBhc3Njb2RlIjoiZTgxZmNjNzc3YjZkNTRiNDlhNTdiZjk4ZGYwY2MwNzhiNDQyZjhiYiIsInBhY2thZ2VfaWQiOiI0ODkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0= |
---|
|
|
Tcia button generator |
---|
label | Search |
---|
url | https://pathdb.cancerimagingarchive.net/imagesearch?f[0]=collection:c_nmc_2019 |
---|
|
|
|
|
...