Improve your knowledge of the algorithm training process with IKOSA AI.
IKOSA AI helps you develop custom 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.
The training accuracy depends on the quantity and quality of annotations.
The minimum training requirements are:
Create a label set includingat 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 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.
Important: if you want to train an algorithm on multichannel images, please select instance segmentation training type as semantic segmentation doesn’t support it.
4. Select labels
Select only the labels relevant to the training.
5. Select dataset split
Random dataset split: selecting your images for training and validation all at once.
Manual dataset split: selecting your images for training and validation separately.
All features belonging to a label must be annotated - features that are present, but not annotated introduce counterfactual information into the training algorithm and impair the results.
Images with annotations are split into training- and validation datasets (depending on which dataset split you choose this will happen automatically or manually) - so be sure to include enough of them.
The larger the used dataset, the better the expected results.
Images without features (e.g. only background, no annotation) can be included as well- these images can help the model distinguish between foreground and background and especially allow for trainings based on label categories very similar to the background.
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. Select images
Select image type
Important: The image type option will only be enabled if an instance segmentation training type has been selected.
Select between 2D and multichannel images.
Selecting images in 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.
Selecting images in manual dataset split
Select at least 1 training and 1 validation image. Both of them must contain annotations.
Important: If images are selected manually, the validation set has to:
include all detectable labels
be similar in composition and quality to the training set
If you are not sure how to conduct manual selection, then we would recommend applying random split since the manual selection of images might introduce unwanted bias.
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 labeled.
The algorithm will autonomously learn how the background differs from the foreground. Even if you do not provide separate background images, the algorithm will obtain information on what the background looks like based on the spaces in between annotations.
Uploading images showing background only helps the algorithm learn what the background looks like, even when there are no objects with labels assigned present in the foreground. This also helps the algorithm learn what patterns form in the background around a foreground object.
You should be careful what unannotated images you include in the training as examples for background only. If foreground objects are present in those images, the algorithm will later not be able to make a proper distinction between background and foreground areas.
Important: Make sure these images contain only background, as unlabeled features introduce counterfactual information into the trained algorithm.
7. Select training duration
For 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.
The extended training option is recommended as a second refinement step and can yield a significant improvement in dice coefficient (depending on the data set) providing you with even better results.
8. Review your selections and start the training
Review your selections before starting your algorithm training.
Give your training a name and start it!
Wait until the training is completed
You can track the progress of your active trainings.
Please note: your training might 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