How Training Works
NeuroQP training learns a staining-specific ON/OFF classifier.
The meaning of ON and OFF depends on the project workflow.
Cell counting and classification projects
In this workflow, cells are detected once from a Cell Detection marker such as DAPI or NeuN.
Marker stainings are then classified against those detected cells.
- ON means the detected cell expresses the selected marker.
- OFF means the detected cell does not express the selected marker.
This workflow has a shared cell denominator, so metrics such as % ON are meaningful.
Independent-detection projects
In this workflow, candidate cells are detected directly from each marker staining.
Training teaches NeuroQP which candidates should count as accepted marker-positive cells.
- ON means accepted marker-positive cell.
- OFF means rejected candidate, false positive, or background.
OFF does not mean a biological marker-negative cell in this workflow. It means the detection should not be counted.
Each staining has its own classifier
Each staining is trained independently.
A model trained for one staining is not the model for another staining.
You need a separate classifier for each staining you want to analyze.
How the model learns
NeuroQP takes labeled examples and uses the image around each detected candidate or cell for training.
The result is a staining-specific classifier that can be applied across slices.
What training cannot fix
Training cannot fix poor image quality, wrong registration, missing images, or inconsistent labels.
If the underlying data or labels are unreliable, the trained model will also be unreliable.
Training is iterative
The normal workflow is to label samples, train a model, inspect the output, add targeted samples, and train again.
The most important quality control is visual review on real slices.