...
IKOSA AI helps you develop custom bio image bioimage analysis algorithms without any coding. To help you achieve this we prepared a simple guide for you to go through the individual steps of your algorithm training.
...
| Table of Contents | ||||||
|---|---|---|---|---|---|---|
|
📑 Preparation for algorithm training
...
Create a label set including at least 1 label (e.g. for a specific cell or tissue type) and
Annotate at least 2 images, meaning all relevant objects you would like to segment have to be annotated and labelled labeled (e.g. all lipid droplets, mitochondria as shown in the figures below)
However, the prerequisites for an accurate application depend largely on the complexity of the use case. The complexity typically increases with the number of labels and the difficulty to differentiate between objects. Greater complexity usually requires more images and more annotations.
...
📑 The IKOSA AI wizard Step-by-step
...
| Note |
|---|
Important:
|
1. Read the introduction
Before starting a new training, you can learn all about IKOSA AI here in this article. The link from the IKOSA AI Wizard leads you to this page.
...
Select the project you have just prepared by annotating/labelling labeling the data.
...
3. Select training type
Currently, IKOSA AI covers semantic segmentation tasks only. However, image classification and object detection will also be supported very soon. If you would like to learn how these differ, please refer to our article on: Algorithm Training Types
...
Select only the labels relevant for to the training.
...
5. Select dataset split
...
| Note |
|---|
Important: if you have annotated your images mostly within ROI(s), then you should consider the option of training your algorithm on ROI(s) instead of the whole image. If an image does not contain any ROI(s) but you still select this function, the whole image will be used instead. |
6. Option a: Random dataset split and
...
select your images
When using a random dataset split, 80% of your selected images will be automatically assigned to the training set, while 20% of the images will be assigned to the validation set, on which the performance of your trained algorithm will be evaluated.
...
As stated above, images (or ROI(s)) without annotations of the selected labels can also be included in the training. This provides the algorithm training with a 'baseline' for a more reliable distinction between the background and features that need to be labelledlabeled.
| Note |
|---|
Important: Make sure these images contain only background, as unlabeled features introduce counterfactual information into the trained algorithm. Make sure that all objects in the selected images/ROI(s) are annotated and labelled correctly. |
7. Select training duration
For a an initial draft of an algorithm, we recommend using the quick training option. In this way, you can quickly get a first impression of the performance of the algorithm and check , if more images need to be included in the training set.
...
You can track the progress of your active trainings. If multiple trainings have been started by you and/or other users (which may not be visible to you), trainings will be queued and an estimated time until the completion of the training is displayed.
...
📑 Overview of the results
As soon as the training is completed, you will receive a notification email. Then you can download the training report and review the algorithm training performance.
...
The downloadable zip - folder contains the report as a PDF file with quantitative outcomes and an extra file with qualitative results or visualizations.
...
If you still have questions regarding your algorithm training, feel free to send us an email at support@ikosa.ai. Copy-paste your training ID in the subject line of your email.
...
📑 Related articles
| Filter by label (Content by label) | ||||||
|---|---|---|---|---|---|---|
|