---
license: mit
dataset_info:
  features:
  - name: subject_id
    dtype: string
  - name: image_number
    dtype: int64
  - name: cell_count
    dtype: int64
  - name: image
    dtype:
      image:
        decode: false
  - name: label
    dtype: string
  - name: class_label
    dtype: string
  - name: fold
    dtype: int64
  - name: original_image_name
    dtype: string
  - name: relative_file_path
    dtype: string
  splits:
  - name: train
    num_bytes: 6487895691.044
    num_examples: 10661
  download_size: 1100428227
  dataset_size: 6487895691.044
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

## 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 | "UID_1_1_1_all.bmp" | Reference to source data |
| `relative_file_path` | String | Path to image relative to dataset root | "fold_0/all/UID_1_1_1_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](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