Datasetpublic

dwb2023/cnmc-leukemia-2019

Dataset Summary This dataset contains microscopic images of white blood cells for the purpose of identifying and classifying Acute Lymphoblastic Leukemia (ALL). It provides a valuable resource for researchers and practitioners in the field of medical imaging and hematology. Field Name Data Type Description Example Value Usage subject_id String Unique identifier for each patient "1", "H24" Patient-level grouping, analysis image_number Integer Sequential number for… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/cnmc-leukemia-2019.

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Dataset Card

Dataset Summary

This dataset contains microscopic images of white blood cells for the purpose of identifying and classifying Acute Lymphoblastic Leukemia (ALL). It provides a valuable resource for researchers and practitioners in the field of medical imaging and hematology.

Field Name	Data Type	Description	Example Value	Usage
`subject_id`	String	Unique identifier for each patient	"1", "H24"	Patient-level grouping, analysis
`image_number`	Integer	Sequential number for images from the same patient	1, 10, 22	Image ordering, tracking
`cell_count`	Integer	Number of cells in the image	1, 2, 12	Feature for analysis/modeling
`image`	Image	Microscopic image of blood cells	(Binary image data)	Input for image analysis
`label`	String	Simple label (cancer/normal)	"cancer", "healthy"	Target variable for classification
`class_label`	String	Alias for `label`	"all", "hem"	Synonym for `label`
`fold`	Integer	Cross-validation fold assignment	0, 1, 2	Model training/evaluation
`original_image_name`	String	Original filename of the image	"UID111all.bmp"	Reference to source data
`relative_file_path`	String	Path to image relative to dataset root	"fold0/all/UID111_all.bmp"	Locating image files

Supported Tasks and Leaderboards

The dataset is well-suited for various machine learning tasks, including:

—Image Classification: Distinguish between ALL and healthy (HEM) cells.
—Object Detection: Locate and count individual cells within the images.
—Segmentation: Delineate the boundaries of individual cells in the images.

The ISBI 2019 ALL Challenge provided a leaderboard to benchmark performance on the classification task. You can find more information about the challenge and its results here: https://doi.org/10.7937/tcia.2019.dc64i46r

Data Splits

The dataset is provided as a single split (train) containing all 10,661 images. Researchers are encouraged to create their own validation and test splits, or utilize the pre-defined folds for cross-validation experiments.

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