Update README.md

README.md

@@ -1,73 +1,125 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: dataset_name
-    dtype: string
-  - name: subject_id
-    dtype: string
-  - name: modality
-    dtype: string
-  - name: orientation
-    dtype: string
-  - name: diagnosis
-    dtype: string
-  - name: pathology_subtype
-    dtype: string
-  - name: lesion_type
-    dtype: string
-  - name: lesion_grade
-    dtype: float32
-  - name: lesion_count
-    dtype: int32
-  - name: has_pathology
-    dtype: bool
-  - name: patient_age
-    dtype: float32
-  - name: patient_sex
-    dtype: string
-  - name: field_strength
-    dtype: string
-  - name: manufacturer
-    dtype: string
-  - name: scanner_model
-    dtype: string
-  - name: repetition_time
-    dtype: float32
-  - name: echo_time
-    dtype: float32
-  - name: flip_angle
-    dtype: float32
-  - name: primary_lesion_hemisphere
-    dtype: string
-  - name: primary_lesion_location
-    dtype: string
-  - name: secondary_lesion_hemisphere
-    dtype: string
-  - name: secondary_lesion_location
-    dtype: string
-  - name: bounding_boxes
-    dtype: string
-  - name: total_lesion_voxels
-    dtype: int32
-  - name: has_enhancing_tumor
-    dtype: bool
-  - name: has_flair_hyperintensity
-    dtype: bool
-  - name: has_necrotic_core
-    dtype: bool
-  - name: has_resection_cavity
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 1681105729.399
-    num_examples: 134201
-  download_size: 1625827790
-  dataset_size: 1681105729.399
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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.