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README.md

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+---
+license: mit
+tags:
+  - medical-imaging
+  - cardiac-mri
+  - segmentation
+  - attention-unet
+  - pytorch
+datasets:
+  - ACDC
+pipeline_tag: image-segmentation
+---
+
+# ACDC Heart Segmentation — Attention U-Net Ensemble
+
+A 5-fold cross-validated **Attention U-Net** ensemble trained on the [ACDC cardiac MRI dataset](https://www.creatis.insa-lyon.fr/Challenge/acdc/) for multi-class segmentation of cardiac structures.
+
+## Model Description
+
+This model segments cardiac MRI short-axis slices into 4 classes:
+- **Class 0**: Background
+- **Class 1**: Right Ventricle (RV)
+- **Class 2**: Myocardium (LVM)
+- **Class 3**: Left Ventricle (LVC)
+
+### Architecture
+- **Base**: U-Net with Attention Gates
+- **Input**: Single-channel grayscale MRI (256x256)
+- **Output**: 4-class segmentation map
+- **Training**: 5-fold cross-validation on the ACDC training set
+
+## Usage
+
+```python
+import torch
+from model import AttentionUNet
+
+model = AttentionUNet(img_ch=1, output_ch=4)
+state_dict = torch.load("fold_1_model.pth", map_location="cpu", weights_only=False)
+if 'model_state_dict' in state_dict:
+    state_dict = state_dict['model_state_dict']
+model.load_state_dict(state_dict)
+model.eval()
+
+# Input: [batch, 1, 256, 256] normalized to mean=0.5, std=0.5
+img_tensor = torch.randn(1, 1, 256, 256)
+with torch.no_grad():
+    output = model(img_tensor)  # [batch, 4, 256, 256]
+    pred = torch.argmax(output, dim=1)  # [batch, 256, 256]
+```
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `model.py` | Model architecture (AttentionUNet) |
+| `fold_1_model.pth` - `fold_5_model.pth` | Trained weights for each CV fold |
+
+## Training Details
+
+- **Dataset**: ACDC (Automated Cardiac Diagnosis Challenge)
+- **Optimizer**: Adam
+- **Loss**: Cross-Entropy + Dice Loss
+- **Image Size**: 256x256
+- **Normalization**: (pixel - 0.5) / 0.5