---
title: BrainVision
emoji: 🐧
colorFrom: gray
colorTo: green
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: true
license: mit
short_description: A Project under Samsung Innovation Campus AI Program
model-index:
- name: BrainVision
  results:
  - task:
      type: image-segmentation
      name: Image Segmentation
    dataset:
      type: lgg-mri-segmentation-dataset
      name: 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|


Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference