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PyTorch implementation of MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

This repository contains the implementation of "MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation" in pytorch.

Paper

Ibtehaz, Nabil, and M. Sohel Rahman. "MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation." Neural Networks 121 (2020): 74-87.

Usage


from multiresunet import MultiResUnet 
net = MultiResUnet(channels=3,filters=16,nclasses=1)



""" Arguments : channels - input image channels filters - filters to begin with (Unet) nclasses - number of classes """

Results

Trained on TGS Salt Identification Challenge data for 100 epochs

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PyTorch implementation of MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation

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