Create README.md

README.md

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+---
+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           | Whole Tumor | Tumor Core | Enhancing Tumor |
+| ---------------- | ----------- | ---------- | --------------- |
+| Dice Coefficient | 0.x        | 0.x       | 0.x            |
+| Hausdorff95 (mm) | 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
+
+```python
+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:
+
+