Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system
Fig 1
Workflow to obtain histological scores from microscopy images of murine lungs by using convolutional neural networks (CNN).
We built two types of models: a CNN to classify the Ashcroft score (used as an example in the figure) and a CNN to classify an inflammation score. A whole slide scan of a mouse lung (left) is divided into smaller image tiles. The tiles are fed into a CNN model and a probability distribution over the image classes is obtained as an output. We used the Inception-V3 CNN architecture, pre-trained on the Image-Net dataset (1.28 106 images) and re-trained on labelled tiles of lung tissue (between 3.5 103 and 1.4 104 images, see Methods). From the probability outputs of the two neural networks, the Ashcroft fibrosis and inflammation scores are computed as the score-weighted sum of the class probabilities after a renormalization to 1 without pignore (see Methods).