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README.md

@@ -1,47 +1,214 @@
 ---
-dataset_info:
-  features:
-  - name: patient_id
-    dtype: int32
-  - name: report_number
-    dtype: string
-  - name: report
-    dtype: string
-  - name: slice_index
-    dtype: int32
-  - name: frame_index
-    dtype: int32
-  - name: is_optimal
-    dtype: bool
-  - name: optimal_slice
-    dtype: int32
-  - name: image_t1
-    dtype: image
-  - name: image_t1ce
-    dtype: image
-  - name: image_t2
-    dtype: image
-  - name: image_flair
-    dtype: image
-  splits:
-  - name: train
-    num_bytes: 155018602.0
-    num_examples: 1660
-  - name: validation
-    num_bytes: 19795441.0
-    num_examples: 210
-  - name: test
-    num_bytes: 18848991.0
-    num_examples: 210
-  download_size: 192849980
-  dataset_size: 193663034.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: cc-by-nc-4.0
+task_categories:
+- image-to-text
+- text-generation
+tags:
+- medical
+- radiology
+- brain-tumor
+- mri
+- report-generation
+pretty_name: BraTS 2020 Radiology Reports
+size_categories:
+- 1K<n<10K
 ---
+
+# BraTS 2020 - Brain Tumor Radiology Report Generation Dataset
+
+## Dataset Description
+
+This dataset contains paired brain MRI scans and radiology reports for training image-to-text models for automated radiology report generation.
+
+### Dataset Summary
+
+- **Total Patients:** 166
+- **Total Images:** 2080
+- **Modalities:** T1, T1ce, T2, FLAIR (4 modalities per frame)
+- **Frames per Patient:** 10 (optimal slice ± neighbors)
+- **Image Size:** 224x224
+- **Source:** BraTS 2020 Challenge
+
+### Splits
+
+| Split | Patients | Images |
+|-------|----------|--------|
+| Train | 166 | 1660 |
+| Validation | 21 | 210 |
+| Test | 21 | 210 |
+
+## Dataset Structure
+
+### Data Fields
+
+- `patient_id` (int): Unique patient identifier
+- `report_number` (str): Original report number (TR001, TR002, etc.)
+- `report` (str): Full radiology report text
+- `slice_index` (int): Axial slice index in the MRI volume
+- `frame_index` (int): Frame number (0-9, where frame ~5 is optimal)
+- `is_optimal` (bool): Whether this is the optimal slice for this patient
+- `optimal_slice` (int): The optimal slice index for this patient
+- `image_t1` (Image): T1-weighted MRI image
+- `image_t1ce` (Image): T1 contrast-enhanced MRI image
+- `image_t2` (Image): T2-weighted MRI image
+- `image_flair` (Image): FLAIR MRI image
+
+### Data Sample
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("KMH158/brats2020_shagufta")
+
+# Access a sample
+sample = dataset['train'][0]
+
+print("Patient ID:", sample['patient_id'])
+print("Report:", sample['report'][:200])
+print("Modalities available:", ['t1', 't1ce', 't2', 'flair'])
+
+# Display images
+from PIL import Image
+import matplotlib.pyplot as plt
+
+fig, axes = plt.subplots(1, 4, figsize=(16, 4))
+for idx, modality in enumerate(['t1', 't1ce', 't2', 'flair']):
+    axes[idx].imshow(sample[f'image_{modality}'], cmap='gray')
+    axes[idx].set_title(modality.upper())
+    axes[idx].axis('off')
+plt.show()
+```
+
+## Methodology
+
+### Slice Selection
+
+The optimal slice for each patient was determined using a trained regression model that:
+1. Predicts tumor burden based on image features
+2. Uses weighted scoring: 3×Necrotic + 2×Edema + 1×Enhancing
+3. Selects the slice with maximum tumor burden
+
+Then, 5 slices before and 4 slices after the optimal slice are extracted for data augmentation.
+
+### Image Preprocessing
+
+1. **Normalization:** Global min-max normalization to [0, 255]
+2. **CLAHE:** Contrast Limited Adaptive Histogram Equalization
+3. **Resizing:** Bicubic interpolation to 224x224
+4. **Format:** RGB (3-channel grayscale)
+
+### Report Mapping
+
+Reports were mapped from the original CSV using the "Number" column (TR001, TR002, etc.) to BraTS patient IDs.
+
+## Intended Use
+
+### Primary Use Case
+
+Training vision-language models for:
+- Automated radiology report generation
+- Medical image captioning
+- Clinical decision support
+
+### Example Models
+
+- BLIP (Salesforce/blip-image-captioning-base)
+- GIT (microsoft/git-base)
+- ViT + GPT-2
+- Custom vision-language architectures
+
+### Example Training Code
+
+```python
+from datasets import load_dataset
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from torch.utils.data import DataLoader
+
+# Load dataset
+dataset = load_dataset("KMH158/brats2020_shagufta")
+
+# Initialize model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# Create dataloader
+def collate_fn(batch):
+    # Use T1ce modality (or any other)
+    images = [item['image_t1ce'] for item in batch]
+    texts = [item['report'] for item in batch]
+    
+    return processor(images=images, text=texts, return_tensors="pt", padding=True)
+
+train_loader = DataLoader(dataset['train'], batch_size=8, collate_fn=collate_fn)
+
+# Training loop
+for batch in train_loader:
+    outputs = model(**batch, labels=batch['input_ids'])
+    loss = outputs.loss
+    # ... training code
+```
+
+## Multi-Modal Usage
+
+This dataset includes all 4 MRI modalities. You can:
+
+1. **Single modality:** Use T1ce (shows enhancement best)
+2. **Multi-modal:** Concatenate or ensemble all 4 modalities
+3. **Modality-specific:** Train separate models per modality
+
+```python
+# Example: Multi-modal input
+sample = dataset['train'][0]
+
+# Stack all modalities
+import numpy as np
+multi_modal = np.stack([
+    np.array(sample['image_t1']),
+    np.array(sample['image_t1ce']),
+    np.array(sample['image_t2']),
+    np.array(sample['image_flair'])
+], axis=-1)  # Shape: (224, 224, 12) - 4 modalities × 3 RGB channels
+```
+
+## Limitations
+
+- Dataset size: 166 patients (relatively small for deep learning)
+- Single institution/protocol
+- Expert annotations needed for validation
+- Bias towards GBM cases in BraTS dataset
+- English reports only
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{brats_radiology_reports_2024,
+  title={BraTS 2020 Radiology Report Generation Dataset},
+  author={Your Name},
+  year={2024},
+  publisher={HuggingFace},
+  howpublished={\url{https://huggingface.co/datasets/KMH158/brats2020_shagufta}}
+}
+
+@article{menze2015multimodal,
+  title={The multimodal brain tumor image segmentation benchmark (BRATS)},
+  author={Menze, Bjoern H and Jakab, Andras and Bauer, Stefan and others},
+  journal={IEEE transactions on medical imaging},
+  volume={34},
+  number={10},
+  pages={1993--2024},
+  year={2015},
+  publisher={IEEE}
+}
+```
+
+## License
+
+CC-BY-NC 4.0 (Non-commercial use only)
+
+## Acknowledgments
+
+- BraTS Challenge organizers
+- Original report annotators
+- HuggingFace for dataset hosting