metadata

license: apache-2.0
tags:
  - brain-mri
  - segmentation
  - medical-imaging
  - deep-learning
  - unet
base_model: tf-keras/imagenet-mobilenetv2
model-index:
  - name: Brain MRI Segmentation - FLAIR Abnormality Segmentation
    results:
      - task:
          type: image-segmentation
          name: Image Segmentation
        dataset:
          name: LGG Segmentation Dataset
          type: medical-imaging
          link: https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
        metrics:
          - type: dice
            value: 0.77
            name: Dice Coefficient
          - type: iou
            value: 0.62
            name: Intersection over Union (IoU)

Brain MRI Segmentation - FLAIR Abnormality Segmentation v1.0.0

This repository hosts the trained model for FLAIR Abnormality Segmentation in Brain MRI scans. The model is a U-Net architecture with a MobileNetV2 encoder pretrained on ImageNet, designed to segment FLAIR abnormalities from MRI images effectively.

Model Details

Architecture: U-Net with MobileNetV2 encoder and custom decoder layers.
Dataset: LGG Segmentation Dataset
Version: v1.0.0
Task: Image Segmentation
License: Apache 2.0

Usage

To use this model for inference, you can load it using the tensorflow library.

# Clones the repository and install dependencies
!git clone https://huggingface.co/preethamganesh/bms-flair-abnormality-segmentation-v1.0.0
!pip install tensorflow

# Imports TensorFlow
import tensorflow as tf

# Loads the pre-trained model from the cloned directory
model_path = "bms-flair-abnormality-segmentation-v1.0.0"
exported_model = tf.saved_model.load(model_path)

# Retrieves the default serving function from the loaded model
model = exported_model.signatures["serving_default"]

# Prepares a dummy input tensor for inference (batch size: 1, height: 256, width: 256, channels: 3)
input_data = tf.ones((1, 256, 256, 3), dtype=tf.float32)

# Performs inference using the model. The output will be a dictionary, with the segmentation map in the key 'output_0'
output = model(input_data)["output_0"]

# Prints the shape of the output tensor for verification
print("Output shape:", output.shape)

Training Details

Compute

The model was trained on a GeForce 4070Ti GPU with 16GB VRAM.
Training completed in approximately 4.9 minutes over 24 epochs.

Dataset

The model was trained on the LGG Segmentation Dataset, which includes Brain MRI images labeled for FLAIR abnormality segmentation.
Only images with positive FLAIR abnormalities were selected for training.

Performance on test set

Dice Coefficient: 0.77
Intersection over Union (IoU): 0.62

Citation

If you use this model in your research, please cite the repository:

@misc{preethamganesh2024brainmri,
  title={Brain MRI Segmentation - FLAIR Abnormality Segmentation},
  author={Preetham Ganesh},
  year={2025},
  url={https://huggingface.co/preethamganesh/brain-mri-flair-abnormality-segmentation-v1.0.0},
  note={Apache-2.0 License}
}

Contact

For any questions or support, please contact [email protected].