Datasets:

liamchalcroft
/

brainbox

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

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

[
  {
    "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.