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---
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license: apache-2.0
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tags:
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- brain-mri
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- segmentation
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- medical-imaging
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- deep-learning
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- unet
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base_model: "tf-keras/imagenet-mobilenetv2"
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model-index:
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- name: Brain MRI Segmentation - FLAIR Abnormality Segmentation
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results:
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- task:
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type: image-segmentation
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name: Image Segmentation
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dataset:
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name: LGG Segmentation Dataset
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type: medical-imaging
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link: https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
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metrics:
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- type: dice
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value: 0.843
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name: Dice Coefficient
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- type: iou
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value: 0.609
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name: Intersection over Union (IoU)
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---
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# Brain MRI Segmentation - FLAIR Abnormality Segmentation v1.0.0
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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.
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## Model Details
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- **Architecture:** U-Net with MobileNetV2 encoder and custom decoder layers.
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- **Dataset:** [LGG Segmentation Dataset](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation)
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- **Version:** v1.0.0
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- **Task:** Image Segmentation
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- **License:** Apache 2.0
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## Performance
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The model achieved the following metrics on the test dataset:
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- **Dice Coefficient:** 0.843
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- **Intersection over Union (IoU):** 0.609
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## Usage
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To use this model for inference, you can load it using the `tensorflow` library.
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```bash
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# Clones the repository and install dependencies
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!git clone https://huggingface.co/preethamganesh/bms-flair-abnormality-segmentation-v1.0.0
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!pip install tensorflow
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# Imports TensorFlow
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import tensorflow as tf
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# Loads the pre-trained model from the cloned directory
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model_path = "bms-flair-abnormality-segmentation-v1.0.0"
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exported_model = tf.saved_model.load(model_path)
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# Retrieves the default serving function from the loaded model
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model = exported_model.signatures["serving_default"]
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# Prepares a dummy input tensor for inference (batch size: 1, height: 256, width: 256, channels: 3)
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input_data = tf.ones((1, 256, 256, 3), dtype=tf.float32)
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# Performs inference using the model. The output will be a dictionary, with the segmentation map in the key 'output_0'
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output = model(input_data)["output_0"]
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# Prints the shape of the output tensor for verification
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print("Output shape:", output.shape)
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```
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## Dataset
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The model was trained on the LGG Segmentation Dataset, which contains Brain MRI images with FLAIR abnormalities labeled for segmentation tasks. The dataset is available on [Kaggle](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation).
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## Citation
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If you use this model in your research, please cite the repository:
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```bash
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@misc{preethamganesh2024brainmri,
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title={Brain MRI Segmentation - FLAIR Abnormality Segmentation},
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author={Preetham Ganesh},
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year={2025},
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url={https://huggingface.co/preethamganesh/brain-mri-flair-abnormality-segmentation-v1-0-0},
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note={Apache-2.0 License}
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}
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```
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## Contact
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For any questions or support, please contact [email protected].
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