Efficient point process inference for large scale object detection
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Updated
Apr 3, 2018 - MATLAB
Efficient point process inference for large scale object detection
A short workshop for Matlab
Count-Ception: Counting by Fully Convolutional Redundant Counting
Medical Image processing and segmentation for the automatic detection and counting of blood platelets and WBCs.
Plugins for ImageJ/FIJI
braincellcount: count cells in mouse brains
A simple ImageJ Macro to count the cells of multiple images.
This program is implemented to count the number of cells in the image. The cells are also labeled and the perimeter and area are calculated for each cell.
Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images
Semi-automated script for detection and quantification of c-Fos cells in IHC stained confocal stack images
An immunohistochemistry cell-counting (quantifying) neural network (CSRNet PyTorch) that was trained on KRT14, KRT5, and Ki67 stains (and of course DAPI).
A demonstration script for analyzing cell density in whole slide images (WSIs). This repository accompanies the article published on daangeijs.nl. The demo showcases how to compute cell density in detected tumor regions using WholeSlideData and GeoPandas.
The code of paper: Lite-UNet: A Lightweight and Efficient Network for Cell Localization
This repository is dedicated to the AIBI 2019/2020 project. This project's objective is to automate cell counting in microscopy images.
Region-based Fitting of Overlapping Ellipses (original implementation by C. Panagiotakis and A.A. Argyros, Image Vis Comput 2020)
SuperDSM is a globally optimal segmentation method based on superadditivity and deformable shape models for cell nuclei in fluorescence microscopy images and beyond.
Cell localization and counting: 1) Exponential Distance Transform Maps for Cell Localization; 2) Multi-scale Hypergraph-based Feature Alignment Network for Cell Localization; 3) Lite-UNet: A lightweight and efficient network for cell localization
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