U-Net for brain MRI | PyTorch
pytorch.org › hub › mateuszbuda_brain-segmentationModel Description. This U-Net model comprises four levels of blocks containing two convolutional layers with batch normalization and ReLU activation function, and one max pooling layer in the encoding part and up-convolutional layers instead in the decoding part. The number of convolutional filters in each block is 32, 64, 128, and 256.
brain-segmentation-pytorch/README.md at master · mateuszbuda ...
github.com › mateuszbuda › brain-segmentationU-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI based on a deep learning segmentation algorithm used in Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. This repository is an all Python port of official MATLAB/Keras implementation in brain-segmentation. Weights for trained models are provided and can be used for inference or fine-tuning on a different dataset.