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 3 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
Apr 11, 2025 - Python
HistoSeg++: Delving deeper with attention and multiscale feature fusion for biomarker segmentation. Accepted in 12th International Conference on Biomedical and Bioinformatics Engineering (ICBBE 2025)
-
Updated
Sep 17, 2025 - Jupyter Notebook
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."