IKOSA AI: Tap the potential of AI in microscopy analysis
IKOSA AI is our specialized software solution for training AI applications that solve concrete life science research problems. The good news is that you can do this without any coding and AI experience or complicated technical steps.
Training microscopy image analysis applications doesn’t have to be hard
IKOSA AI is our advanced deep learning solution powered by convolutional neural networks (CNNs). The smart technology behind it can autonomously learn from image input data. Based on this information the neural networks in the background create applications well-suited for analyzing a variety of new microscopy image samples.
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All you need to do to get a fully-functional image analysis application is perform a number of easy-to-follow steps
To experience artificial intelligence in microscopy image analysis, start your IKOSA AI trial!
Reasons to choose IKOSA AI
There are a number of reasons to start using IKOSA AI in your lab. We design each feature in dialogue with leading researchers from the biomedical field to constantly improve the performance of our software product. Here’s why you will benefit from IKOSA AI.
IKOSA AI helps you get ahead in research
Forget about the hassle of conventional methods for analyzing microscopy images. Get fast and reliable results and speed up your discovery process.
Applications trained with IKOSA AI yield highly accurate results thanks to our unique deep learning technology.
IKOSA AI is designed for maximum flexibility
No programming required
IKOSA AI equips you to develop fully-functional applications on an intuitive interface. You don’t have to code and are perfectly independent in the process.
Read about IKOSA AI in our blog
What IKOSA AI users have to say
“Manual analysis of histological slides is time consuming. The platform IKOSA AI is a web-based application allowing location-independent automated processing of data. Using the option to develop your own AI for specific research questions, we managed to quickly produce observer-independent, quantifiable data from a large set of histological slides by automatic detection of lymphatic vessels, regenerating axons as well as specific muscle fiber types”.
Dr. David Hercher
Master the art of training applications for microscopy image analysis
The IKOSA Knowledge Base is our extensive collection of educational materials, where we address common questions our users ask. Our instructional articles on IKOSA AI will help perform any app training task with ease.
Witness IKOSA AI in action
Request a demo to view application training in the works. Our sales team guides you through all the essential steps.
Our FAQ section addresses all your concerns about AI application training
Our team of AI and computational microscopy experts provides answers to your burning questions.
Which image analysis tasks can we automate with the help of IKOSA AI?
Can we use an existing Prisma application in IKOSA AI?
Using existing Prisma applications in IKOSA AI is possible, if they fit your research question and image dataset. If this is not the case, some modification and fine-tuning are required. Our team offers you assistance in this regard.
What data has been used for training the Prisma software solutions?
Each Prisma app has been trained on a set of microscopy images provided by our cooperation partners. This means that established life science researchers have evaluated the image data used in the training process and approved its suitability for a particular use case.
Can we retrain our applications to better fit our research design?
You can retrain an existing neural network using a new set of images. This method allows you to minimize prediction errors associated with your app and ensure that it better fits your research question. If you are unsure how to retrain an existing application with IKOSA AI, our team is there to help you.
Who has access to our applications trained with IKOSA AI?
Only you and the members of your IKOSA organization have access to your trained applications. You have the option to assign specific access permissions to users within your organization.
You can easily download and share output results with your other team members who are not registered IKOSA users.
To protect the confidentiality of your work, ready-made algorithms cannot be shared with other IKOSA organizations.
What input data do we need to train image analysis applications with IKOSA AI?
To train a neural network in IKOSA AI you have to provide a small set of representative images for your specific use case. The images included in your training set must contain the objects you want to analyze. IKOSA AI supports microscopy images with varying formats, imaging modalities and resolution.
What if our training dataset does not include enough images?
Using IKOSA AI you will be able to train an algorithm even on a small set of microscopy images. This works even with datasets as small as 10 images.
How do we figure out what aspects of our app need retraining?
If your application makes the same mistakes repeatedly, this can often be due to omitting certain objects of interest in your input image data. Try to identify objects that are not recognized properly and label more of them in your input images before you submit the app for retraining.
How do we interpret the output results?
In the results output report you get a detailed overview of the images, labels and regions-of-interest (ROIs) you’ve included in the training. Based on the quantitative and qualitative information given you can evaluate the performance of your application on your own.
The output report also provides helpful visualizations that will help you assess your results at a glance.
With the help of the “positive” and “negative” outputs listed you can easily estimate the correctness of the predictions made by your app.
Can we import our own applications?
You have an app developed using a different method and want to retrain it on the IKOSA Platform?
Currently you cannot do this automatically in your IKOSA account. However, with a little technical assistance from our team this is possible.
Can we export ready-made IKOSA AI apps to other locations?
Not currently, but an export option to the most popular deep learning software systems is foreseen in the future.
How can we update existing apps?
Once trained, IKOSA AI applications do not automatically adapt to new image data. To keep your app on par with your data, you have to regularly retrain it.
How much time does it take to train an application with IKOSA AI?
The time to train a deep neural network with IKOSA AI varies depending on the complexity of your analysis tasks and the quality of your microscopy image data.
If you go for a quick training you can get a basic artificial intelligence app up and running in 20-30 minutes. An extended training can take up to a couple of hours. Altogether with fine-tuning and re-training you will have a fully-functional analysis application within a day or two.
How many input images do we need to train image analysis applications with IKOSA AI?
This varies depending on your research design and the quality of your input data. However, we advise you to provide at least 5-10 input images as a start.
How do we decide which images to include in the app training?
Select images that best represent your sample and contain variations of the morphological features you want to examine. The more diverse input image data you provide, the more robust and accurate your application will be.
How does outcome validation work with IKOSA AI?
To make sure your app works properly, you can compare your results with that of conventional methods like manual analysis, rule-based system or semi-automated methods. Don’t forget to use an identical set of microscopy images in each of the cases.
On our end, we provide a training report as well as visualization and performance metrics of the validation dataset.
If application training with IKOSA AI is carried out by the user, does this mean that subjective human bias cannot be ruled out in the results?
In the training process a number of experts within your team have to annotate objects of interest in your image data and assign labels to them. Based on this set of labels and annotations neural networks learn to attribute objects to particular categories. Since the artificial intelligence network adopts the common consensus among these experts, it tends to yield more accurate predictions than manual methods.
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