Algorithm development for quantitative digital microscopy made easy

  • Complete 6 main ​phases of the training process
  • ​Wait until IKOSA AI​ trains your algorithm
  • ​Check the ​outcomes in the report of your training
  • ​If you are satisfied with the results, then simply start using your algorithm!
  • ​​If not, then analyze the training results by means of provided visualisations and adjust your training accordingly.
PIC

For more information on algorithm training, interpretability of results and validation, please refer to our FAQ.

FAQS

If you are interested in specific examples of deep learning algorithm training, visit our case study page.

​Case Studies

Benefits of IKOSA ​Portal

Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble
Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble
Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble
Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble
Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble
Upload your standard or whole slide images to your storage and assign them to specific projects. Data uploads are resumable to save you time and trouble

Leverage the power of deep learning in microscopy

Accelerate research outcomes with IKOSA AI. Simply ​contact us to request a demo or a one month free guided trial version!

​Contact us

​Check our FAQ-section to learn more about IKOSA AI

Still wondering whether IKOSA AI is the best deep learning solution for your research project? Or you still need some answers regarding the built-in capabilities of IKOSA AI and how they can help you enhance research performance? Our team of AI and computational microscopy experts answers your burning questions.

​​All important whys and hows ​about algorithms

Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.
Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.
Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.
Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.
Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.
Deep learning in biomedical imaging relies on trained neural networks in order to recognize objects and patterns, for instance in microscopy image data. In general, deep learning is a subfield of machine learning. The most frequently used neural network types for image analysis are convolutional neural networks (CNN), a multi-layer neural network model capable of capturing hierarchical representations of the image input very well.

Book a demo

Talk to our company representatives to learn more about the AI solutions

​ ​Booking options

​Try yourself

​Create a free account and test all the ​AI image analysis applications for free.

​ Sign up

© 2016-2022 Inventigen Technologies Pvt. Ltd.