README.md

metadata

license: apache-2.0

Model Overview

• Model Name: ImprovedUNet3D
• Architecture: 3D U-Net with residual-style encoder-decoder blocks, instance normalization, LeakyReLU activations, and dropout
• Framework: PyTorch
• Input Channels: 4 (e.g., multimodal MRI inputs)
• Output Channels: 4 (segmentation classes)
• Base Filters: 16 (scalable by multiplier in constructor)

Intended Use

• Primary Application: Brain tumor segmentation on 3D MRI volumes using the BraTS 2020 dataset.
• Users: Medical imaging researchers, AI practitioners in healthcare.
• Out-of-Scope: Medical diagnosis without expert oversight. Not for real-time intraoperative use.

Training Data

• Dataset: Medical Segmentation Decathlon / BraTS 2020 training and validation sets

• Source: awsaf49/brats20-dataset-training-validation on Kaggle

• Data Volume: ~369 cases (training + validation)

• Preprocessing:

  • Skull stripping
  • Intensity normalization per modality
  • Resampling to uniform voxel size
  • Patching or cropping to fixed volume shape

Performance

Metric	NNE Tumor Core	Peritumoral Edema	Enhancing Tumor	Background
Dice Coefficient	0.6448	0.7727	0.8026	0.9989
Hausdorff95 (mm)	x	x	x	x

Limitations and Risks

• Overfitting: Model may not generalize to scanners or protocols outside BraTS.
• Data Imbalance: Rare tumor subregions may have lower performance.
• Clinical Use: Intended for research only; does not replace expert radiologist interpretation.

How to Use

from improved_unet3d import ImprovedUNet3D
import torch

# Instantiate model
model = ImprovedUNet3D(in_channels=4, out_channels=4, base_filters=16)
# Load pretrained weights (if available)
model.load_state_dict(torch.load("path/to/checkpoint.pth"))
model.eval()

# Inference on a single 3D volume
input_volume = torch.randn(1, 4, 128, 128, 128)  # example shape
with torch.no_grad():
    output = model(input_volume)
# output shape: [1, 4, 128, 128, 128]

Training Details

• Optimizer: Adam
• Learning Rate: 1e-4
• Batch Size: 2
• Loss Function: Combined Dice + Cross-Entropy
• Epochs: 200
• Scheduler: Cosine annealing or Step LR

Ethical Considerations

• Bias: Trained on a specific dataset; demographic coverage may be limited.
• Privacy: Data must be anonymized. Users should ensure HIPAA/GDPR compliance.

Citation

If you use this model, please cite: