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+checkpoints

README.md

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+---
+language:
+- en
+tags:
+- medical-segmentation
+- pytorch
+- brats
+- unet
+- mri
+- brain-tumor
+license: mit
+metrics:
+- val_loss: 0.012861
+model-index:
+- name: PicoUNet
+  results: []
+---
+
+# PicoUNet for BraTS 2020 Tumor Segmentation
+
+This model is a deep learning model for 3D/2D brain tumor segmentation, trained on the **BraTS 2020** dataset. It uses a **PicoUNet** architecture (a lightweight variant of U-Net) to segment brain tumors from MRI scans.
+
+## Model Description
+
+- **Architecture**: PicoUNet (Encoder-Decoder with Skip Connections)
+- **Input Modalities**: 2 Channels (FLAIR, T1Ce) -> *Note: Standard BraTS has 4 (T1, T1Ce, T2, FLAIR), this model uses a subset.*
+- **Output Classes**: 4 classes (mapped from BraTS labels)
+  - **Class 0**: Background
+  - **Class 1**: Necrotic and Non-Enhancing Tumor Core (NCR/NET) - *BraTS label 1*
+  - **Class 2**: Edema (ED) - *BraTS label 2*
+  - **Class 3**: Enhancing Tumor (ET) - *BraTS label 4* (Label 4 is mapped to 3)
+
+## Dataset
+
+The model fits on the **MICCAI BraTS 2020 Challenge Data**.
+The dataset consists of multimodal MRI scans of glioblastoma (GBM/HGG) and lower-grade glioma (LGG).
+All volumes are registered to the same template space (SRI24) and interpolated to the same resolution ($1 mm^3$).
+
+### Preprocessing
+- **Slicing**: 2D axial slices were extracted.
+- **Resizing**: Images were resized to 128x128.
+- **Normalization**: Min-Max normalization per slice.
+
+## Training Configuration
+
+Auto-generated from the model checkpoint:
+- **Epochs Trained**: 20
+- **Validation Loss**: 0.012861
+- **Experiment Name**: Brats-UNet
+- **Batch Size**: 64
+- **Learning Rate**: 0.03
+- **Seed**: 42
+- **Optimizer**: SGD (Momentum 0.9)
+- **Loss Function**: CrossEntropyLoss (or DiceLoss depending on experiment)
+
+## Usage
+
+To use this model, you need to preprocess your input image similarly to the training pipeline (extract 2 channels: FLAIR and T1Ce, resize to 128x128).
+
+```python
+import torch
+from miccai_brats.models.unet.unet import PicoUNet
+
+# 1. Instantiate Model
+model = PicoUNet(in_channels=2, num_classes=4)
+
+# 2. Load Checkpoint
+checkpoint = torch.load("best_model.pth", map_location="cpu")
+model.load_state_dict(checkpoint['model_state_dict'])
+
+model.eval()
+
+# 3. Inference
+input_tensor = torch.randn(1, 2, 128, 128) # Fake Batch of [FLAIR, T1Ce]
+with torch.no_grad():
+    output = model(input_tensor)
+    prediction = torch.argmax(output, dim=1)
+
+print("Predicted shape:", prediction.shape) # [1, 128, 128]
+```
+
+## Intended Use & Limitations
+- **Research Use Only**: This model is for educational and research purposes. It is not intended for clinical use.
+- **Performance**: This is a "Pico" variant, optimized for speed and low compute, not for state-of-the-art accuracy.
+
+## Citations
+If you use the BraTS dataset, please cite:
+1. Menze et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE TMI 2015.
+2. Bakas et al. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 2017.
+3. Bakas et al. "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv 2018.

best_model.pth

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