| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: volume_id |
| dtype: string |
| - name: slice_id |
| dtype: int32 |
| - name: pre |
| dtype: image |
| - name: post1 |
| dtype: image |
| - name: pre_series |
| dtype: string |
| - name: post1_series |
| dtype: string |
| - name: mask |
| dtype: image |
| - name: is_tumorous |
| dtype: bool |
| - name: menopause |
| dtype: int32 |
| - name: race |
| dtype: int32 |
| - name: er |
| dtype: int32 |
| - name: pr |
| dtype: int32 |
| - name: her2 |
| dtype: int32 |
| - name: tubule_tumor_grade |
| dtype: int32 |
| - name: nuclear_tumor_grade |
| dtype: int32 |
| - name: mitotic_tumor_grade |
| dtype: int32 |
| - name: nottingham_grade |
| dtype: int32 |
| - name: breast_density |
| dtype: int32 |
| - name: recurrence |
| dtype: int32 |
| splits: |
| - name: train |
| num_bytes: 27873069458 |
| num_examples: 156202 |
| download_size: 30304389430 |
| dataset_size: 27873069458 |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-classification |
| - image-segmentation |
| - object-detection |
| language: |
| - en |
| tags: |
| - mri |
| - medical |
| - breast |
| - cancer |
| - tumor |
| pretty_name: Duke Breast Cancer MRI |
| size_categories: |
| - 100K<n<1M |
| --- |
| # DUKE-BREAST-CANCER-MRI — 2D Slice-Based Multisequence Breast MRI Dataset with Lesion Segmentation |
|
|
| This dataset is derived from the **Duke Breast Cancer MRI** collection, a large-scale breast MRI dataset with pathologically confirmed lesions, hosted on **The Cancer Imaging Archive (TCIA)**. The original collection provides multi-sequence 3D breast MRI volumes with bounding-box lesion annotations and comprehensive clinical, pathological, and molecular metadata. |
| Here, we provide a **2D slice-based version**, extracted from two of the available MRI sequences per patient — **pre-contrast T1-weighted (pre)** and **post-contrast T1-weighted (post1)** — paired slice-for-slice with the corresponding lesion segmentation mask and patient-level clinical/pathological metadata. |
| The dataset is designed for multimodal breast lesion segmentation, classification, and prognostic modeling on breast MRI. |
|
|
|
|
| ## 📦 Dataset Structure |
|
|
| The dataset has a single `train` split, with each row corresponding to a single 2D axial slice from a patient's coregistered pre / post1 volumes: |
|
|
| | Field | Description | |
| |---|---| |
| | `volume_id` | Unique patient/case identifier (e.g., `Breast_MRI_001`) | |
| | `slice_id` | Index of the axial slice within the volume | |
| | `pre` | 2D pre-contrast T1-weighted MRI slice | |
| | `post1` | 2D post-contrast T1-weighted MRI slice (first post-contrast acquisition) | |
| | `pre_series` | Series UID identifier for the pre-contrast sequence | |
| | `post1_series` | Series UID identifier for the post1-contrast sequence | |
| | `mask` | 2D binary lesion segmentation mask (bounding box annotation) | |
| | `is_tumorous` | Boolean — whether a lesion is present on this slice | |
| | `menopause` | Menopausal status: `0` (pre-menopausal), `1` (post-menopausal) | |
| | `race` | Patient race/ethnicity (encoded categorical) | |
| | `er` | Estrogen receptor status: `0` (negative), `1` (positive) | |
| | `pr` | Progesterone receptor status: `0` (negative), `1` (positive) | |
| | `her2` | HER2 status: `0` (negative), `1` (positive), `2` (equivocal) | |
| | `tubule_tumor_grade` | Tubule formation grade (Nottingham): `1`, `2`, or `3` | |
| | `nuclear_tumor_grade` | Nuclear pleomorphism grade (Nottingham): `1`, `2`, or `3` | |
| | `mitotic_tumor_grade` | Mitotic count grade (Nottingham): `1`, `2`, or `3` | |
| | `nottingham_grade` | Combined Nottingham histologic grade: `1`, `2`, or `3` | |
| | `breast_density` | Breast tissue density (BI-RADS): `1`, `2`, `3`, or `4` | |
| | `recurrence` | Binary recurrence outcome: `0` (no recurrence), `1` (recurrence) | |
|
|
| `menopause`, `race`, `er`, `pr`, `her2`, `tubule_tumor_grade`, `nuclear_tumor_grade`, `mitotic_tumor_grade`, `nottingham_grade`, `breast_density`, and `recurrence` are patient-level attributes and are therefore identical across all slices belonging to the same `volume_id`. |
|
|
|
|
| ## ⚙️ Preprocessing |
|
|
| - For each patient, preprocessed **pre-contrast T1** and **post1-contrast T1** DICOM series were extracted and converted to image format |
| - Volumes were sliced into 2D axial slices; the bounding-box lesion segmentation mask was created per slice from annotated lesion regions |
| - Patient-level clinical and pathological metadata (`menopause`, `race`, `er`, `pr`, `her2`, tumor grades, `breast_density`, `recurrence`) were broadcast from the Duke Breast Cancer MRI clinical database to every slice belonging to that patient |
| - All slices (lesion-containing and background) were exported; use `is_tumorous` to filter or balance |
| - The following patient cases were excluded during processing: `Breast_MRI_065`, `Breast_MRI_120`, `Breast_MRI_279`, `Breast_MRI_596`, `Breast_MRI_700`, `Breast_MRI_433`, `Breast_MRI_627` |
|
|
| --- |
|
|
| ## 🚀 Usage |
|
|
| ```python |
| from datasets import load_dataset |
| import matplotlib.pyplot as plt |
| import numpy as np |
| |
| ds = load_dataset("YOUR_USERNAME/DUKE_BREAST_CANCER_MRI", split="train") |
| |
| # Grab a lesion-containing slice and view both sequences plus the mask overlay |
| sample = next(s for s in ds if s["is_tumorous"]) |
| |
| fig, axes = plt.subplots(1, 3, figsize=(14, 4)) |
| |
| # Pre-contrast with mask |
| axes[0].imshow(sample["pre"], cmap="gray") |
| axes[0].imshow(np.array(sample["mask"]), cmap="jet", alpha=0.4) |
| axes[0].set_title("Pre-Contrast + Mask") |
| axes[0].axis("off") |
| |
| # Post1-contrast with mask |
| axes[1].imshow(sample["post1"], cmap="gray") |
| axes[1].imshow(np.array(sample["mask"]), cmap="jet", alpha=0.4) |
| axes[1].set_title("Post1-Contrast + Mask") |
| axes[1].axis("off") |
| |
| # Enhancement map (post1 - pre) |
| enhancement = np.array(sample["post1"]) - np.array(sample["pre"]) |
| axes[2].imshow(enhancement, cmap="RdBu_r") |
| axes[2].set_title("Enhancement (Post1 - Pre)") |
| axes[2].axis("off") |
| |
| # Clinical and pathological metadata |
| info_text = ( |
| f"{sample['volume_id']} | Slice {sample['slice_id']}\n" |
| f"ER: {sample['er']}, PR: {sample['pr']}, HER2: {sample['her2']}\n" |
| f"Nottingham Grade: {sample['nottingham_grade']} | " |
| f"Breast Density: {sample['breast_density']}\n" |
| f"Recurrence: {sample['recurrence']}" |
| ) |
| fig.suptitle(info_text, fontsize=10) |
| plt.tight_layout() |
| plt.show() |
| ``` |
|
|
| --- |
|
|
| ## 🏷️ Feature Descriptions |
|
|
| ### Receptor Status |
| Molecular markers predictive of treatment response and prognosis: |
| - **ER (Estrogen Receptor)**: `0` = Negative, `1` = Positive |
| - **PR (Progesterone Receptor)**: `0` = Negative, `1` = Positive |
| - **HER2 (Human Epidermal Growth Factor Receptor 2)**: `0` = Negative, `1` = Positive, `2` = Equivocal |
|
|
| ### Nottingham Histologic Grading System |
| Combined assessment of tumor differentiation and proliferation. Comprises three components, each graded 1–3: |
| - **Tubule Formation** (`tubule_tumor_grade`): Percentage of tumor forming tubular structures |
| - **Nuclear Pleomorphism** (`nuclear_tumor_grade`): Degree of variation in nuclear size and shape |
| - **Mitotic Count** (`mitotic_tumor_grade`): Number of mitotic figures per high-power field |
|
|
| **Combined Grade** (`nottingham_grade`): Sum of components; `1–3` (favorable), `4–5` (intermediate), `6–9` (unfavorable) |
|
|
| ### Breast Density |
| BI-RADS density classification (tissue composition affecting lesion detectability): |
| - `1` = Almost entirely fat |
| - `2` = Scattered fibroglandular densities |
| - `3` = Heterogeneously dense |
| - `4` = Extremely dense |
|
|
| ### Menopausal Status |
| - `0` = Pre-menopausal |
| - `1` = Post-menopausal |
|
|
| ### Recurrence Outcome |
| - `0` = No recurrence during follow-up |
| - `1` = Recurrence during follow-up |
|
|
|
|
| ## 🧪 Use Cases |
|
|
| - Multimodal (pre / post1-contrast T1) breast lesion segmentation and detection |
| - Breast cancer receptor status (ER/PR/HER2) classification from imaging |
| - Pathologic grade prediction from MRI |
| - Prognostic modeling: predicting recurrence risk from imaging and clinical features |
| - Enhancement kinetics analysis: examining lesion enhancement patterns across pre/post acquisitions |
| - Multi-task learning combining segmentation with receptor status, grade, and recurrence prediction |
| - Representation learning and self-supervised pretraining for breast cancer MRI |
| - Breast density characterization and tissue composition analysis |
|
|
|
|
| ## ⚠️ Important Notes |
|
|
| - **Class imbalance**: most slices, particularly toward the superior/inferior extremes of a volume, contain no lesion. Use `is_tumorous` to filter or apply weighted sampling. |
| - **Volume-level splits**: always split train/val/test at the `volume_id` level, never at the slice level, to avoid data leakage between sets. |
| - **Lesion annotations**: lesion regions are provided as bounding-box masks; voxels within the bounding box may include surrounding tissue rather than tumor proper. |
| - **Mask-image alignment**: `mask` is in the same 2D slice space as `pre` and `post1` for a given row, so no additional registration is needed to overlay it. |
| - **Missing data**: a small number of cases may have incomplete clinical or pathological information; refer to the original TCIA collection for metadata details. |
| - **Patient exclusions**: seven patient cases were excluded from this release. |
|
|
|
|
| ## 📚 Citation |
|
|
| This dataset is derived from data made available on **The Cancer Imaging Archive (TCIA)** under a **CC BY-NC 4.0** license. |
| If you use this dataset, please acknowledge [chehablab.com](Chehab Lab) and cite the original Duke collection: |
|
|
| ```bibtex |
| @misc{Whissel2022, |
| author = {Whissel, C. M. and Georgiade, G. S. and Sinha, S. and Sinha, U. P.}, |
| title = {Duke Breast Cancer MRI [dataset]}, |
| year = {2022}, |
| publisher = {The Cancer Imaging Archive}, |
| doi = {10.7937/tcia.d8xv-ke92} |
| } |
| ``` |
|
|
| --- |
|
|
| ## 📜 License |
|
|
| This dataset is released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license, consistent with the source data on TCIA. |
|
|
| You may copy, modify, distribute, and use the data **for non-commercial purposes only**, **provided that appropriate credit is given** to the original authors, TCIA, and the Duke Breast Cancer MRI collection. |
|
|
| [](https://creativecommons.org/licenses/by-nc/4.0/) |
|
|
| **Chehab Lab @ 2026** |
