This repository contains my implementations of the algorithms which we used for evaluation of the MoNuSeg challenge at MICCAI 2018.
-
Updated
Jul 22, 2021 - Jupyter Notebook
#
monuseg-challenge
Here are 2 public repositories matching this topic...
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
-
Updated
Oct 26, 2024 - Python
Improve this page
Add a description, image, and links to the monuseg-challenge topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the monuseg-challenge topic, visit your repo's landing page and select "manage topics."