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README.md

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