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---
license: mit
tags:
- vision
- image-segmentation
- unet
- resnet
- medical
- pytorch
model-index:
- name: BrainVision
results:
- task:
type: image-segmentation
name: Image Segmentation
dataset:
name: LGG MRI Segmentation Dataset
type: lgg-mri-segmentation-dataset
split: test
metrics:
- type: dice_score
value: 0.8898
name: Dice Score
- type: iou_score
value: 0.8015
name: IoU Score
- type: accuracy
value: 0.9963
name: Accuracy
- type: precision
value: 0.882
name: Precision
- type: recall
value: 0.8978
name: Recall
---
# Brain Tumor Segmentation
#### Model Description
This model is a U-Net based deep learning model for the segmentation of brain tumors from 2D MRI slices. It is an end-to-end segmentation model designed to assist radiologists by automatically identifying and outlining tumorous regions. The model leverages transfer learning by using a pre-trained ResNet50 backbone as its encoder.
#### Intended Use & Limitations
This model is intended for research and educational purposes. It is a tool for exploring deep learning for medical image segmentation.
Limitations:
- The model was trained on a specific dataset and may not generalize well to other datasets from different scanners, protocols, or patient populations.
- It should not be used for clinical diagnosis without validation by a qualified medical professional.
#### Training Data
The model was trained on the [LGG MRI Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation) from Kaggle.
- Volume: 3,929 MRI images from 110 patients.
- Annotation: Expert-annotated binary masks for tumor regions.
- Image Size: 256x256 pixels with 3 channels (converted to RGB).
#### Training Procedure
The model was trained using a two-stage fine-tuning approach with PyTorch.
Model Architecture:
- Architecture: U-Net
- Encoder: ResNet50 with imagenet pre-trained weights.
- Decoder: A symmetric expanding path with skip connections.
- Output: A single-channel probability map.
Hyperparameters:
- Optimizer: AdamW
- Loss Function: A composite loss function combining Dice Loss and Binary Cross-Entropy (BCE).
$$L_{\text{combo}} = L_{\text{dice}} + L_{\text{BCE}}$$
- Epochs:
- Stage 1 (Frozen Encoder): 5 epochs at a learning rate of 1e−4.
- Stage 2 (Fine-tuning): 15 epochs at a learning rate of 1e−5.
- Learning Rate Scheduler: ReduceLROnPlateau with patience=3, factor=0.1.
- Batch Size: 16
#### Evaluation Results
The model was evaluated on an unseen test set composed of 10% of the patient data.
| Metric | Value |
| :--- | :--- |
| Dice Score | 0.8898 |
| IoU Score | 0.8015 |
| Accuracy | 0.9963 |
| Precision | 0.8820 |
| Recall | 0.8978| |