| --- |
| language: |
| - en |
| tags: |
| - medical |
| - brain |
| - mri |
| - neuroimaging |
| - pathology |
| - object-detection |
| - bounding-boxes |
| - vision-language |
| - multimodal |
| - fine-tuning |
| task_categories: |
| - object-detection |
| - image-to-text |
| - visual-question-answering |
| modality: |
| - image |
| - text |
| size_categories: |
| - 100K<n<1M |
| pretty_name: BrainBox |
| --- |
| |
| # BrainBox |
|
|
| A unified medical imaging dataset containing 134,201 brain MRI slices with bounding box annotations, designed for fine-tuning vision-language models (VLMs) on medical image localization tasks. |
|
|
| ## Dataset Summary |
|
|
| BrainBox consolidates multiple 2D and 3D medical imaging datasets into a standardized 2D format optimized for VLM training. Each image is paired with structured metadata and precise bounding box annotations for pathological findings. |
|
|
| - **Total Images**: 134,201 2D MRI slices |
| - **Source Datasets**: 8 publicly available medical imaging datasets |
| - **Modalities**: T1w, T1c, T2w, FLAIR, DWI, ADC, TRACE |
| - **Orientations**: Axial, Coronal, Sagittal |
| - **Pathologies**: Stroke (68,136), Glioma (57,964), Meningioma (4,168), Pituitary Adenoma (3,933) |
|
|
| ## Dataset Purpose |
|
|
| This dataset serves as a fine-tuning corpus for vision-language models to perform: |
| - Medical image interpretation and localization |
| - Pathology detection with spatial grounding |
| - Multimodal medical question answering |
| - Cross-modal medical image understanding |
|
|
| ## Data Processing |
|
|
| - **3D to 2D Conversion**: 3D volumes converted to optimal 2D slices using intelligent slice selection |
| - **Standardization**: Unified schema across heterogeneous source datasets |
| - **Quality Control**: Verified image paths and validated bounding box annotations |
| - **Format**: JPG images (256×256) with JSON bounding box metadata |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| dataset = load_dataset("liamchalcroft/brainbox") |
| |
| # Example: Load an image with its annotations |
| sample = dataset["train"][0] |
| image = sample["image"] |
| diagnosis = sample["diagnosis"] |
| bboxes = json.loads(sample["bounding_boxes"]) # Parse JSON string |
| ``` |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `image` | Image | 2D MRI slice (JPG format) | |
| | `diagnosis` | string | Primary medical diagnosis | |
| | `pathology_subtype` | string | Specific pathology classification | |
| | `lesion_type` | string | General lesion category (neoplasm/vascular_lesion) | |
| | `bounding_boxes` | string | JSON array of lesion bounding boxes | |
| | `modality` | string | MRI sequence type | |
| | `orientation` | string | Slice orientation | |
| | `has_pathology` | bool | Presence of pathological findings | |
| | `subject_id` | string | Anonymized subject identifier | |
| | Additional fields | - | Demographics, imaging parameters, anatomical locations | |
|
|
| ## Bounding Box Format |
|
|
| ```json |
| [ |
| { |
| "x_min": 128, "y_min": 62, |
| "x_max": 139, "y_max": 66, |
| "width": 11, "height": 4, "area": 44 |
| } |
| ] |
| ``` |
|
|
| ## Applications |
|
|
| - **VLM Fine-tuning**: Train vision-language models for medical image understanding |
| - **Object Detection**: Develop pathology localization models |
| - **Medical AI**: Build diagnostic assistance systems |
| - **Research**: Cross-modal medical image analysis studies |
|
|
| ## Data Distribution |
|
|
| - **Vascular Lesions**: 68,136 images (acute, chronic, subacute stroke) |
| - **Neoplasms**: 66,065 images (glioma, meningioma, pituitary adenoma) |
| - **Balanced Orientations**: ~33% each (axial, coronal, sagittal) |
| - **Multi-modal**: 7 MRI sequence types |
|
|
| ## Limitations |
|
|
| - Converted to 2D format (original 3D context reduced) |
| - Heterogeneous source data quality |
| - Limited demographic metadata coverage |
| - Bounding boxes simplified from original segmentation masks |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original source datasets. This is a processed compilation for VLM research purposes. |
|
|
| ## License |
|
|
| Individual source datasets retain their original licenses. Verify licensing terms for your specific use case. |
