Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system
Fig 5
Comparison of CNN and human expert Ashcroft scores and analysis of amount of data required for training.
A. Comparison of the Ashcroft score performed by a human pathologist vs the Ashcroft scored by the CNN based algorithm. Each value is the mean over a whole lung slice from experiments (n = 72) where animals obtained varying doses of Bleomycin to trigger lung fibrosis. Both curves are in good correlation (r2 = 0.92), with a slope m close to 1 and a y-intercept b close to 0 (m = 1.07 ± 0.04, b = -0.04 ±0.08, fit parameters are optimum fit parameters ± the difference at the 5% and 95% confidence intervals.) B. Dependency of the accuracy of the Ashcroft fibrosis CNN model on the amount of training data available. A(n) / Amax compares the accuracy A(n) of a model trained using n randomly selected images to the accuracy Amax of a model trained all available labelled images (n = 12000). The dashed line is an empirical fit using an asymptotic function.