---
license: mit
---

# RadFig-classifier

A deep learning model for classifying medical images as suitable or unsuitable for Visual Question Answering (VQA) tasks. This classifier helps filter medical images to identify those that are appropriate for VQA applications.

## Overview

RadFig-classifier is based on EfficientNetV2-S architecture and trained on medical imaging data to determine whether an image contains sufficient visual information for meaningful question-answering tasks. The model uses ensemble prediction across 5-fold cross-validation models for robust performance.

## Installation

### Requirements

```bash
pip install torch torchvision timm opencv-python albumentations pandas tqdm pillow numpy
```

### Command Line Usage

#### Single Image Classification

```bash
# Get probability score
python inference.py --input image.jpg

# Get binary classification
python inference.py --input image.jpg --binary
```

#### Batch Processing

```bash
# Process all images in directory
python inference.py --input /path/to/images/ --output results.csv

# Binary classification with CSV output
python inference.py --input /path/to/images/ --output results.csv --binary
```

## Model Architecture

- **Base Model**: EfficientNetV2-S
- **Input Size**: 512×512 pixels
- **Output**: Single probability score (0-1)
- **Training**: 5-fold cross-validation ensemble
- **Framework**: PyTorch + timm

## Directory Structure

```
RadFig-classifier/
├── inference.py           # Main inference script
├── models/                # Pre-trained model weights
│   ├── tf_efficientnetv2_s.in21k_ft_in1k_fold0_best_loss.pth
│   ├── tf_efficientnetv2_s.in21k_ft_in1k_fold1_best_loss.pth
│   ├── tf_efficientnetv2_s.in21k_ft_in1k_fold2_best_loss.pth
│   ├── tf_efficientnetv2_s.in21k_ft_in1k_fold3_best_loss.pth
│   └── tf_efficientnetv2_s.in21k_ft_in1k_fold4_best_loss.pth
├── README.md
└── requirements.txt
```

## Output Format

### Single Image Output

```
Image: medical_scan.jpg
Probability suitable for VQA: 0.8542
Classification: suitable
```

### CSV Output

| image_path | filename | prediction | suitable_for_vqa |
|------------|----------|------------|------------------|
| /path/img1.jpg | img1.jpg | 0.8542 | True |
| /path/img2.jpg | img2.jpg | 0.2156 | False |
| /path/img3.jpg | img3.jpg | 0.9234 | True |

## Command Line Arguments

| Argument | Description | Required |
|----------|-------------|----------|
| `--input` | Input image file or directory | Yes |
| `--models` | Directory containing model files | No (default: "models") |
| `--output` | Output CSV file path | No |
| `--binary` | Return binary predictions instead of probabilities | No |

## Use Cases

- **Medical VQA Systems**: Pre-filter images before VQA processing
- **Dataset Curation**: Automatically filter medical image datasets
- **Quality Control**: Assess image quality for medical AI applications
- **Research**: Filter images for medical computer vision studies

## Citation

If you use RadFig-classifier in your research, please cite:

```bibtex
coming soon...
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.