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--- |
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tags: |
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- pytorch |
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- medical-imaging |
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- semantic-segmentation |
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- cardiac-mri |
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- densenet |
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- computer-vision |
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license: mit |
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datasets: |
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- cardiac-mri |
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metrics: |
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- dice-coefficient |
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- iou |
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library_name: pytorch |
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--- |
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# Cardiac Pathology Prediction - DenseNet |
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## Model Description |
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This is a custom-implemented **DenseNet** for cardiac MRI segmentation and pathology prediction. The model performs semantic segmentation of cardiac structures (left ventricle, right ventricle, and myocardium) from short-axis cardiac cine MR images. |
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### Key Features |
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- 🏗️ Fully Convolutional DenseNet architecture implemented from scratch |
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- 🫀 4-class semantic segmentation (background, LV, RV, myocardium) |
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- 🔬 Trained on NIfTI format cardiac MRI data |
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- 📊 Combined Cross-Entropy and Dice Loss |
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- 🎯 Designed for cardiac pathology classification |
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## Architecture |
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The model follows a U-Net style encoder-decoder architecture with dense blocks: |
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- **Input**: Single-channel 2D cardiac MRI slices (128×128) |
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- **Encoder**: 3 dense blocks (3, 4, 5 layers) with transition down |
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- **Bottleneck**: Dense block with 8 layers |
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- **Decoder**: 3 dense blocks (5, 4, 3 layers) with skip connections |
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- **Output**: 4-channel probability map (softmax activated) |
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- **Growth rate**: 8 (bottleneck: 7) |
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## Intended Use |
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This model is designed for: |
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- Cardiac MRI segmentation research |
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- Educational purposes in medical image analysis |
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- Baseline comparison for cardiac segmentation tasks |
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- Feature extraction for cardiac pathology classification |
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**Note**: This model is for research purposes only and not intended for clinical use. |
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## Training Details |
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- **Loss Function**: Combined Cross-Entropy and Dice Loss (α=0.25) |
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- **Optimizer**: Adam |
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- **Framework**: PyTorch 2.6.0 |
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- **Data Format**: NIfTI (.nii) files |
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- **Image Size**: 128×128 pixels |
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- **Preprocessing**: ROI extraction, normalization, data augmentation |
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## How to Use |
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```python |
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import torch |
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from densenet.densenet import DenseNet |
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# Load the model |
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model = DenseNet() |
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model.load_state_dict(torch.load('model_weights.pth', map_location='cpu')) |
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model.eval() |
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# Inference |
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with torch.no_grad(): |
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# input_image: (1, 1, 128, 128) tensor |
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output = model(input_image) # (1, 4, 128, 128) |
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prediction = torch.argmax(output, dim=1) # Get class predictions |
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``` |
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## Model Files |
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- `model_weights.pth`: Complete model checkpoint including weights and optimizer state |
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- `densenet/`: Source code for the DenseNet architecture |
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- Full implementation available at: [GitHub Repository](https://github.com/NicolasNoya/CardiacPathologyPrediction) |
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## Citation |
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If you use this model, please cite the original paper that inspired this implementation: |
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``` |
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Densely Connected Fully Convolutional Network for Short-Axis Cardiac Cine MR Image |
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Segmentation and Heart Diagnosis Using Random Forest |
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``` |
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## Author |
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**Francisco Nicolás Noya** |
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- GitHub: [@NicolasNoya](https://github.com/NicolasNoya) |
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- Project: IMA205 Challenge |
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## License |
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MIT License - See repository for details. |
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## Disclaimer |
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This model is provided for research and educational purposes only. It has not been clinically validated and should not be used for medical diagnosis or treatment decisions. |
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