---
license: gpl
task_categories:
- text-classification
- image-classification
language:
- en
tags:
- biology
- medical
- Blood
- Cell
- EHR
- Cancer
- Detection
- Multimodal
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': basophil
          '1': erythroblast
          '2': monocyte
          '3': myeloblast
          '4': seg_neutrophil
  - name: label_name
    dtype: string
  - name: ehr_json
    dtype: string
  - name: ehr_age
    dtype: int32
  - name: ehr_gender
    dtype: string
  - name: ehr_country
    dtype: string
  - name: ehr_urban_rural
    dtype: string
  - name: ehr_facility_level
    dtype: string
  - name: ehr_insurance_type
    dtype: string
  - name: ehr_primary_language
    dtype: string
  - name: ehr_time_to_presentation_days
    dtype: int32
  - name: ehr_triage_acuity
    dtype: string
  - name: ehr_provider_specialty
    dtype: string
  - name: ehr_symptoms
    dtype: string
  - name: ehr_medications
    dtype: string
  - name: ehr_diagnosis_suspected
    dtype: string
  - name: lab_wbc
    dtype: float32
  - name: lab_hemoglobin
    dtype: float32
  - name: lab_platelets
    dtype: float32
  - name: lab_neutrophils_pct
    dtype: float32
  - name: lab_lymphocytes_pct
    dtype: float32
  - name: lab_monocytes_pct
    dtype: float32
  - name: lab_eosinophils_pct
    dtype: float32
  - name: lab_basophils_pct
    dtype: float32
  - name: lab_blasts_pct
    dtype: float32
  splits:
  - name: train
    num_bytes: 132423885.0
    num_examples: 5000
  download_size: 128651669
  dataset_size: 132423885.0
---
# Dataset Card: Blood cell images for cancer detection (with Synthetic Hematology EHR, Bundled Parquet)

# Summary

This dataset, "Blood cell images for cancer detection," bundles hematology smear images and synthetic, Africa-aware hematology EHR per image into a single Parquet optimized for multimodal ML. Each image captures detailed cellular morphology under standardized conditions, focusing on both normal and abnormal blood cells.

Each row contains:
- `image` struct with `{bytes, path}`
- `label` (int) and `label_name` (string; inferred from folder)
- `ehr_json` (full EHR record as JSON string)
- Flattened EHR summary fields (demographics, triage, provider, inferred diagnosis)
- Complete blood count (CBC) and differential fields tailored to the image class

All EHR data are fully synthetic and non-identifiable.

This release contains 5,000 images (.jpg), with 1,000 images per class.

### Technical Specifications (declared)

- Resolution: ≥ 1024×1024 pixels
- Staining: Wright–Giemsa
- Magnification: 100× oil immersion (≈1000× total)
- Color: 24-bit RGB
- Multiple focal planes per sample

### Quality Measures (declared)

- Expert hematopathologist validation
- Standardized imaging conditions
- Multiple samples per cell type
- Detailed preparation documentation
- Complete technical metadata

### Clinical Applications (intended)

- Normal vs. abnormal cell differentiation
- Leukemia subtype identification
- Disease progression monitoring
- Early detection screening
- Treatment response assessment

### Image Annotations Include (scope)

- Nuclear patterns and contours
- Cytoplasmic features
- Nucleoli presence
- Cell measurements
- Abnormal inclusions (e.g., Auer rods)

### Machine Learning Capabilities

- Automated cell classification
- Quantitative feature analysis
- Differential counting
- Morphological abnormality detection

## 2) Classes and Counts

Discovered from folder names under `Blood Cell images for Cancer detection/`:
- `basophil`: 1,000
- `erythroblast`: 1,000
- `monocyte`: 1,000
- `myeloblast`: 1,000
- `seg_neutrophil`: 1,000

Total rows: 5,000 (1,000 per class)

## 3) Schema (Bundled Parquet)

- `image`: struct<`bytes`: binary, `path`: string>
- `label`: int32
- `label_name`: string
- `ehr_json`: large_string
- EHR summaries (examples):
  - `ehr_age`, `ehr_gender`, `ehr_country`, `ehr_urban_rural`
  - `ehr_facility_level`, `ehr_insurance_type`, `ehr_primary_language`
  - `ehr_time_to_presentation_days`, `ehr_triage_acuity`, `ehr_provider_specialty`
  - `ehr_symptoms`, `ehr_medications`, `ehr_diagnosis_suspected`
- Labs (flattened):
  - `lab_wbc`, `lab_hemoglobin`, `lab_platelets`
  - `lab_neutrophils_pct`, `lab_lymphocytes_pct`, `lab_monocytes_pct`, `lab_eosinophils_pct`, `lab_basophils_pct`, `lab_blasts_pct`

The full EHR JSON schema is documented inline in the generator and aligns with typical CBC + differential and basic encounter context.

## EHR Synthesis (Class-Conditional)

Hematology-aware logic infers plausible trends:
- `myeloblast` → elevated `blasts_pct` (AML suspected), thrombocytopenia, lower Hb
- `basophil` → basophilia, higher WBC/platelets (CML suspected)
- `monocyte` → monocytosis (CMML or infection suspected)
- `erythroblast` → anemia trend with nucleated RBC context (hemolysis/MDS suspected)
- `seg_neutrophil` → neutrophilia (bacterial infection suspected)

Demographics reflect Africa context (country, urban/rural, facility level, insurance, language) with longer presentation delays in rural/primary settings.

## Coherence Audit (Passed)

Global delays (mean days):
- rural: 21.24  
- urban: 7.85  
- primary: 17.48  
- tertiary: 9.70

Per-class means and fractions:
- `basophil` (n=1000):
  - hb=11.51, wbc=20.50, plt=379.1, neut=60.2, lymph=22.2, mono=6.7, eos=3.2, baso=6.5, blasts=1.1
  - frac_blasts>=20%=0.000, frac_baso>=2%=0.999, frac_neut>=65%=0.231, frac_mono>=10%=0.093
- `erythroblast` (n=1000):
  - hb=9.06, wbc=8.04, plt=241.2, neut=53.7, lymph=32.4, mono=8.6, eos=3.2, baso=1.1, blasts=1.1
  - frac_blasts>=20%=0.000, frac_baso>=2%=0.052, frac_neut>=65%=0.005, frac_mono>=10%=0.311
- `monocyte` (n=1000):
  - hb=11.02, wbc=12.24, plt=246.6, neut=51.2, lymph=22.6, mono=20.4, eos=3.1, baso=1.0, blasts=1.6
  - frac_blasts>=20%=0.000, frac_baso>=2%=0.052, frac_neut>=65%=0.021, frac_mono>=10%=0.996
- `myeloblast` (n=1000):
  - hb=9.54, wbc=18.45, plt=74.7, neut=23.0, lymph=20.7, mono=7.5, eos=2.1, baso=1.1, blasts=45.6
  - frac_blasts>=20%=1.000, frac_baso>=2%=0.076, frac_neut>=65%=0.000, frac_mono>=10%=0.213
- `seg_neutrophil` (n=1000):
  - hb=12.03, wbc=13.04, plt=282.1, neut=76.7, lymph=14.5, mono=4.4, eos=2.9, baso=1.0, blasts=0.5
  - frac_blasts>=20%=0.000, frac_baso>=2%=0.025, frac_neut>=65%=0.995, frac_mono>=10%=0.000

These align with intended clinical logic and thresholds (e.g., blasts in myeloblast, basophils in basophil, neutrophils in seg_neutrophil, anemia in erythroblast), and rural/primary delays > urban/tertiary.


## Ethics and Limitations

- Fully synthetic EHR; no PHI.  
- Trends are plausibility-driven and class-conditional; not diagnostic or epidemiologic evidence.  
- Africa profile encodes general patterns (delays, access) and may not capture country-specific nuances.  
- Rule-based generation cannot fully replicate real-world correlations.


## License and Contact

- See repository LICENSE. If absent, assume “all rights reserved.”
- For questions or calibration requests, open an issue in this repository.