Labeling best practices
In order to get the highest accuracy out of your AI model, it is incredibly important to ensure that labels are created with the highest quality. Even just a few bad labels can dramatically affect the performance of a model. We've compiled a list of things that are very important to ensure that your model is accurate.

Feasibility

One quick sanity test for whether or not an AI analysis will be able to analyze your image is whether or not the objects are distinguishable by the human eye. If they are not possible to distinguish, it is likely very difficult to create a high quality set of labels, which will make AI performance poor.

Label all objects of the same class in all finalized images

This doesn't mean labeling all classes of objects present in your dataset. If you have different types of objects in your images (i.e. glomeruli, tubules, single cells, cancer regions, etc in renal tissue slides), you can pick one class and complete the labels for that one class (ex: glomeruli) in your ground truth images. Once you finish labeling all of the objects from that class (i.e. glomeruli), you can safely submit them to AI training without including labels for any other classes (i.e. tubules, single cells, cancer regions, etc.).
In fact, we suggest starting with a few object classes (i.e. glomeruli and tubules) or one object class for initial AI training, then adding more objects in your subsequent training.
In order to achieve good AI performance, all of images in the Labeled column on the Dashboard must be labeled. If some images are missing labels, it will result in poor AI performance. If you have half-labeled images, make sure to remove them from the Completed column before proceeding to training.
When training, our optimization algorithms penalize the model for predicting an object where there is no label. If you miss labels on your images, you should expect significantly reduced performance.

Label all objects accurately

When you are annotating objects of interest, you need to ensure that you outline the object consistently and accurately. For instance, if you are annotating glomeruli in histology images, you should outline the glomerulus around the edge without overestimating or underestimating the edges.
Examples of good and bad annotations.
The most important thing is to be consistent with how you create your labels. The model will try to make predictions that are similar to the labels that you create.

Include objects at the edge of the images

When objects of interest are cut off by the edges of an image and partially visible, it is extremely important not to omit them during annotation.
There are two objects of interest (glomeruli) at the edge of the image shown in the example below. Despite the fact that they are partially visible, we annotated them properly.
Copy link
On this page
Feasibility
Label all objects of the same class in all finalized images
Label all objects accurately
Include objects at the edge of the images