This repository contains my implementations of the algorithms which we used for evaluation of the MoNuSeg challenge at MICCAI 2018.
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Updated
Jul 22, 2021 - Jupyter Notebook
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monuseg
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HistoSeg is an Encoder-Decoder DCNN which utilizes the novel Quick Attention Modules and Multi Loss function to generate segmentation masks from histopathological images with greater accuracy. This repo contains the code to Test and Train the HistoSeg
segmentation
image-segmentation
unet
attention-mechanism
glas
semantic-segmentation
focal-loss
self-attention
medical-image-segmentation
histological-images
deeplab-v3-plus
segmentation-models
unet-segmentation
dice-loss
monuseg
monuseg-challenge
histology-images
nuclie-segmentation
histological-image-segmentation
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Updated
Oct 26, 2024 - Python
UNet++, UNet, SegNet and DeepLabv3 implemented in Keras for MoNuSeg dataset
computer-vision
deep-learning
neural-network
unet
segnet
semantic-segmentation
keras-tensorflow
deeplabv3
unetplusplus
monuseg
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Updated
Apr 6, 2020 - Jupyter Notebook
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