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shalabh147 / Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks

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Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.

deep-neural-networks deep-learning medical-imaging segmentation dice-scores keras-tensorflow survival-models dice-coefficient brain-tumor-segmentation unet-3d cnn-segmentation vnet3d survival-prediction glioma mri-segmentation dice-loss brats-dataset aiformedicine unet-architecture
  • Updated Jul 24, 2020
  • Jupyter Notebook
Retina_Blood_Vessel_Segmentation

YoussefAboelwafa / Retina_Blood_Vessel_Segmentation

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The project involves using a dataset of 100 high-resolution retinal fundus images for blood vessel segmentation to aid in early detection of retinal pathologies | Implemented U-Net architecture from scratch, known for its efficiency in semantic segmentation with limited data | The final model achieved 86% IoU score | PyTorch Lightning

computer-vision cnn image-segmentation unet-architecture
  • Updated Aug 5, 2024
  • Python

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