Figure 3.

The impact of stain normalization on the accuracy of an ML pipeline. After 430 epochs, training on stain-normalized images leads to a 35% lower validation loss (A) and an improvement in F1 score of 11 percentage points (B). The CNN architecture used for these experiments is described in section 3.4.