# TC-SemiSAM3-new

Semi-supervised SAM3 model for coronary vessel segmentation.

## Model Description

This is the final checkpoint from semi-supervised training using:
- 5 labeled videos
- 31 unlabeled videos
- Mean Teacher framework with temporal consistency

## Usage

```python
import torch

# Load checkpoint
checkpoint = torch.load("checkpoint_final.pt", map_location="cpu")

# Get student model weights
state_dict = checkpoint["student_state_dict"]

# Load into SAM3 model
model.load_state_dict(state_dict, strict=False)
```

## Training Details

- Framework: Mean Teacher + Temporal Consistency
- Labeled data: 5 videos (140 frames)
- Unlabeled data: 31 videos
- Confidence-aware regularization enabled

## Performance

| Dataset | Dice | clDice |
|---------|------|--------|
| 36 Training Videos | 0.6811 | 0.7492 |
| 36 Videos (Adaptive) | 0.7232 | 0.7775 |

## Related

- GitHub: https://github.com/qimingfan10/TC-SemiSAM.git