Open-source U-Net 2D Attention Model - Free Deployment to Boost Precise MRI Tumor Segmentation (Compatible with BraTS Dataset)

Unet2dattentionmodel

Developed by AndyWu0719

This is a UNet 2D attention model for MRI tumor segmentation, suitable for the BraTS dataset.

Downloads 90

Release Time : 12/18/2024

Model Overview

This model is based on the UNet architecture with an added attention mechanism, specifically designed for tumor segmentation tasks in MRI images.

Attention mechanism

Incorporates an attention mechanism within the UNet architecture to enhance the model's focus on important features.

Medical image specialization

Optimized specifically for tumor segmentation tasks in MRI medical images.

Lightweight architecture

Based on a 2D UNet architecture, requiring fewer computational resources compared to 3D models.

Medical image segmentation

Tumor region identification

MRI image analysis

Medical imaging analysis

Brain tumor segmentation

Precise segmentation of brain tumor regions on the BraTS dataset.

Segmentation accuracy can be evaluated using metrics such as the Dice coefficient.

Property	Details
Model Type	UNet 2D Attention
Task	MRI Tumor Segmentation
Framework	Tensorflow
Tags	image-segmentation, unet, attention
Datasets	BraTS
Metrics	dice, precision, sensitivity, specificity
Pipeline Tag	image-segmentation

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