Improve your knowledge of the application training process with IKOSA AI.
...
You can train your applications on 2D or multichannel images.
| Note |
|---|
Important: You cannot train your app on time series image. |
Semantic Segmentation | Instance Segmentation | |
|---|---|---|
2D | 2D | Multichannel |
standard and WSI RGB and grayscale 8bit | standard and WSI RGB and grayscale 8bit and 16bit | standard and WSI RGB 8bit and 16bit max. 10 channels |
...
...
📍 Project preparation rules
Use data with a high diversity of feature appearances.
Larger datasets provide better training results. However, small and medium datasets will work just fine if you prepare your images correctly.
The greater complexity of your data requires more images.
...
How to draw ROI(s) for application training with IKOSA AI?
...
📍 Image preparation rules
🏷️ Label set
Create a label set including at least 1 label (e.g. for a specific cell, tissue, or staining type)
| Info |
|---|
Please note If you're training an app with multiple labels, aim to balance the number of annotations across each label. The closer the number of annotations per label is to one another, the better the application's performance will be. |
✏️Labeling, annotation and regions of interests
Use fully annotated and labeled images or ROIs to train your app.
...
Moreover, the training will run through such “multiplied” annotations more often than the other ones, effectively giving them a higher importance in the training process.
...
👁️ Background recognition
The background of an image is an area that DOESN’T CONTAIN the features required for the app training. You add them to help the app differentiate between background and features to recognize, especially when they are very similar.
Background images/ROIs can be added at step 6 of the training process.
| Info |
|---|
FAQ Is it an obligation to have background images or background ROIs? No! The trained app will autonomously learn how the background differs from the features. Even if you do not provide separate background images or background ROIs, the app will obtain information on what the background looks like based on the spaces in between annotations. I don’t provide any background images or background ROIs. What is the sufficient amount of background area within an image or an ROI? Whether you use ROI(s) or not, the greater the amount of non-annotated background area available for application training, the better the resulting model will be. What if I don’t have images only with background? You can dedicate some images that have background and present features:
|
...
| Note |
|---|
Important:Ensure that both background images or background ROIs contain only background, as any unintentionally included unlabeled features introduce misleading information into the trained application. |
...