Supervised learning


Yet another pixel classifier Yapic is a deep learning tool to :

train your own filter to enhance the structure of your choice 

train multiple filter at once 

it is based on the u-net convolutional filter . 

To train it : annotation can come from example from Ilastik software , tif labelled files can be transferred to yapic. 

Training takes about hours to days , prediction takes seconds once trained .

It can be ran from command line .

note that only 10 to 20 images with sparse labeling are required for efficient training 

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ZEN and APEER – Open Ecosystem for integrated Machine-Learning Workflows

Open ecosystem for integrated machine-learning workflows to train and use machine-learning models for image processing and image analysis inside the ZEN software or on the APEER cloud-based platform

Highlights ZEN

  • Simple User Interface for Labeling and Training
  • Engineered Features Sets and Deep Feature Extraction + Random Forrest for Semantic Segmentation
  • Object Classification workflows
  • Probability Thresholds and Conditional Random Fields
  • Import your own trained models as *.czann files (see: czmodel · PyPI)
  • Import "AIModel Containes" from arivis AI for advanced Instance Segmentation
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets and Tile Images
  • open and standardized format to store trained models
ZEN Intellesis Segmentation

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ZEN Intellesis - Pretrained Networks

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Intellesis Object Classification

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Highlights Aarivis AI

  • Web-based tool to label datasets to train Deep Neural Networks
  • Fully automated hyper-parameter tuning
  • Export of trained models for semantic segmentation and AIModelContainer for Instance Segmentation
Annotation Tool

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QuPath is open source software for Quantitative Pathology. QuPath has been developed as a research tool at Queen's University Belfast.


It is a trainable interest point (anatomical landmarks) detection algorithm. It requires images and interest point coordinates. It can run independantly (using csv files to describe coordinates) or it can be executed using Cytomine.


Typical application: Morphometric studies (e.g. in zebrafish/drosphila development)


Used in: Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge Automatic localization of interest points in zebrafish images with tree-based methods 

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Landmark detection example

Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

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