---
model_name: brats-segresnet-monai
tags:
- image-segmentation
- segresnet
- medical-imaging
- brats
- fp16
license: apache-2.0
language:
- en
pipeline_tag: image-segmentation
library_name: monai
---

# 🧠 SegResNet — BraTS Brain Tumor Segmentation (3D, FP16)

This model fine-tunes **SegResNet** for **3D brain tumor segmentation** (Tumor Core, Whole Tumor, Enhancing Tumor) using the  
[BraTS2020 Dataset](https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation).  
The model was trained with **mixed precision (FP16)** using MONAI and Accelerate for efficient multi-GPU training.

---

## ⚙️ Configuration

| Attribute | Value |
|------------|-------|
| **Base Model** | `SegResNet` (MONAI) |
| **Dataset** | [BraTS2020 (Kaggle)](https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation) |
| **Epochs** | 150 |
| **Batch Size (per GPU)** | 2 |
| **ROI Size** | 128×128×128 |
| **Optimizer** | Adam (LR=0.0001, WD=1e-05) |
| **Precision** | FP16 (mixed=True) |
| **Framework** | MONAI + Accelerate |
| **Device** | Multi-GPU (num_processes=2) |

---

## 🩺 Example Segmentation

### 2D Prediction
![2D Prediction](https://huggingface.co/Jesteban247/brats-segresnet-monai/resolve/main/Pred_2d.png)

### 3D Prediction
![3D Prediction](https://huggingface.co/Jesteban247/brats-segresnet-monai/resolve/main/Prediction_3D.png)

---

## 📈 Results (Final Test Metrics)

| Metric | Average | Tumor Core (TC) | Whole Tumor (WT) | Enhancing Tumor (ET) |
|--------|---------|-----------------|------------------|----------------------|
| **Dice** | 0.789 | 0.775 | 0.874 | 0.719 |
| **Precision** | 0.847 | 0.856 | 0.938 | 0.749 |
| **Recall** | 0.771 | 0.762 | 0.839 | 0.713 |
| **Accuracy** | 0.987 | 0.999 | 0.999 | 0.964 |
| **Loss** | 0.192 | - | - | - |

> FP16 mixed precision enabled faster training while maintaining high segmentation accuracy.  
> No data augmentation was used to preserve original image fidelity.