| --- |
| license: other |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - ultrasound |
| - breast |
| - segmentation |
| - busi |
| size_categories: |
| - n<1K |
| --- |
| |
| # BUSI — Breast Ultrasound Images Dataset |
|
|
| Re-hosted mirror of the Breast Ultrasound Images Dataset (Al-Dhabyani et al., 2020), |
| collected at Baheya Hospital, Cairo, Egypt and originally distributed via the |
| Cairo University Scholar page of co-author Aly Fahmy. |
|
|
| This mirror is intended for use with [EasyMedSeg](https://github.com/) and provides: |
|
|
| - A single canonical source (the original Cairo Univ. URL has unstable TLS). |
| - Parquet schema with one row per image and a single binary mask. |
| - For images with multifocal lesions, the multiple `_mask_N.png` files in the |
| original release are merged into one binary mask via per-pixel logical OR |
| (the convention adopted by the paper and by BUS-Set / Thomas et al. 2023). |
|
|
| ## Composition |
|
|
| | Class | Images | Multifocal cases | |
| |-------------|-------:|-----------------:| |
| | benign | 437 | 16 | |
| | malignant | 210 | 1 | |
| | normal | 133 | 0 | |
| | **Total** | **780**| **17** | |
|
|
| Image dimensions are variable (~190–719 px, average ~500×500 px). Source release |
| is from 2018; 600 female patients, ages 25–75; GE LOGIQ E9 / E9 Agile B-mode. |
|
|
| ## Splits |
|
|
| BUSI has **no official train/val/test split**. This release ships a single |
| `train` split. Downstream code is expected to define its own splits |
| (BUS-Set provides reproducible ones). |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |--------------------------|-----------|------------------------------------------------------------| |
| | `image` | `Image` | Source PNG (RGB) | |
| | `mask` | `Image` | Binary mask (`L` mode, 0/255) — OR-merged across all mask files | |
| | `class_label` | `string` | `"benign"` / `"malignant"` / `"normal"` | |
| | `image_id` | `string` | e.g., `"benign (100)"` | |
| | `has_multifocal_lesion` | `bool` | `True` if the source had >1 mask file | |
|
|
| ## Known data-quality caveats |
|
|
| Per Pawłowska et al. (2023, *Data in Brief*, [PMC10293973](https://pmc.ncbi.nlm.nih.gov/articles/PMC10293973/)): |
| - ~235 duplicated images (~19%); 8 lesions appear in both benign and malignant folders (label leakage). |
| - ~70 axilla (non-breast) images mis-categorized as breast. |
| - 295 images with overlaid annotations (calipers, doppler markers, text) intersecting the lesion ROI. |
| - ≥7 images contain a visible biopsy needle. |
|
|
| This mirror **does not modify** the source files (other than mask merging) — the |
| above issues are inherited as-is. Cite Pawłowska et al. when reporting cleanups. |
|
|
| ## License |
|
|
| The original authors do not state an explicit license on the Cairo Univ. page or |
| in the *Data in Brief* paper. Treat as **research use, with required citation**. |
| Kaggle uploaders have tagged copies as CC BY 4.0 (uploader-asserted, not |
| author-confirmed). Do not assume redistribution rights beyond research. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{Aldhabyani2020BUSI, |
| title = {Dataset of breast ultrasound images}, |
| author = {Al-Dhabyani, Walid and Gomaa, Mohammed and Khaled, Hussein and Fahmy, Aly}, |
| journal = {Data in Brief}, |
| volume = {28}, |
| pages = {104863}, |
| year = {2020}, |
| doi = {10.1016/j.dib.2019.104863} |
| } |
| ``` |
|
|
