README.md

---
language:
- en
tags:
- medical
- radiology
- image-captioning
- multimodal
- computer-vision
task_categories:
- image-to-text
- text-generation
pretty_name: "ROCOv2 Modality-Specific Splits"
size_categories:
- 1K<n<10K
---

# ROCOv2 Modality-Specific Dataset Splits

## Dataset Description

This dataset contains modality-specific splits of the ROCOv2 radiology dataset, organized and processed for training specialized medical image captioning models.

### Dataset Summary

- **Total Samples**: 1,000
- **Modalities**: 5
- **Splits per Modality**: train, validation, test
- **Random Seed**: 42
- **Processing Date**: 2025-08-31 12:52:59.233482

### Modality Distribution

| Modality | Samples | Percentage |
|----------|---------|------------|
| CT | 188 | 18.8% |
| X-ray | 199 | 19.9% |
| MRI | 200 | 20.0% |
| Microscopy | 207 | 20.7% |
| Ultrasound | 206 | 20.6% |


## Dataset Structure

### Data Fields

- **caption** (string): Medical description of the image
- **modality** (string): Imaging modality (CT, MRI, X-ray, Ultrasound, Microscopy, Other)
- **modality_id** (int): Numerical ID for the modality
- **caption_length** (int): Number of words in the caption
- **length_category** (string): Caption length category (short, medium, long)
- **original_index** (int): Index in the original dataset
- **image** (Image): Medical image (if available)

### Data Splits

Each modality is split into:
- **Train**: ~80% for model training
- **Validation**: ~10% for model validation
- **Test**: ~10% for final evaluation

### Split Statistics

#### CT

- **train**: 150 samples
- **val**: 18 samples
- **test**: 20 samples

#### X-ray

- **train**: 159 samples
- **val**: 19 samples
- **test**: 21 samples

#### MRI

- **train**: 160 samples
- **val**: 20 samples
- **test**: 20 samples

#### Microscopy

- **train**: 165 samples
- **val**: 20 samples
- **test**: 22 samples

#### Ultrasound

- **train**: 164 samples
- **val**: 20 samples
- **test**: 22 samples



## Usage

```python
from datasets import load_dataset

# Load a specific modality
ct_dataset = load_dataset("WafaaFraih/rocov2-modality-splits", "CT")

# Load all modalities
all_datasets = {}
modalities = ["CT", "MRI", "X-ray", "Ultrasound", "Microscopy", "Other"]

for modality in modalities:
    try:
        all_datasets[modality] = load_dataset("WafaaFraih/rocov2-modality-splits", modality)
        print(f"Loaded {modality}: {len(all_datasets[modality]['train'])} train samples")
    except:
        print(f"{modality} not available")

# Access specific split
train_data = ct_dataset['train']
val_data = ct_dataset['validation'] 
test_data = ct_dataset['test']
```

## Modality-Specific Characteristics

### CT (Computed Tomography)
- Keywords: axial, coronal, sagittal, contrast, enhancement, density, hounsfield
- Typical caption prefixes: "This CT scan shows", "CT imaging reveals"

### MRI (Magnetic Resonance Imaging)  
- Keywords: t1, t2, flair, dwi, gadolinium, signal, intensity, weighted
- Typical caption prefixes: "This MRI shows", "Magnetic resonance imaging reveals"

### X-ray (Radiography)
- Keywords: ap, lateral, pa, portable, upright, supine, opacity, lucency
- Typical caption prefixes: "This chest X-ray shows", "Radiographic examination reveals"

### Ultrasound (Sonography)
- Keywords: echo, doppler, transducer, frequency, acoustic, anechoic, hyperechoic
- Typical caption prefixes: "This ultrasound shows", "Sonographic examination reveals"

### Microscopy (Histopathology)
- Keywords: cellular, nuclear, cytoplasm, tissue, staining, morphology, biopsy
- Typical caption prefixes: "This microscopic image shows", "Histological examination reveals"

## Training Recommendations

### Modality-Specific Training
- Each modality has optimized hyperparameters
- Use modality-specific data augmentation
- Consider modality-specific evaluation metrics

### Batch Sizes (recommended)
- CT: 8
- MRI: 6  
- X-ray: 10
- Ultrasound: 8
- Microscopy: 6

### Learning Rates (recommended)
- CT: 2e-5
- MRI: 1.5e-5
- X-ray: 3e-5
- Ultrasound: 2.5e-5
- Microscopy: 1.8e-5

## Citation

If you use this dataset, please cite the original ROCOv2 paper and this processed version:

```
@article{rocov2,
  title={ROCOv2: Radiology Objects in COntext Version 2},
  author={...},
  journal={...},
  year={2023}
}
```

## License

Please refer to the original ROCOv2 dataset license terms.

## Dataset Creation

This dataset was created by processing the original ROCOv2 dataset with stratified splitting to ensure balanced representation across different caption lengths and modalities.

### Processing Steps:
1. Modality classification based on caption content
2. Stratified splitting by caption length categories
3. Quality validation and metadata generation
4. Conversion to Hugging Face Dataset format

## Contact

For questions about this processed dataset, please open an issue in the repository.