The notebooks use the dataset from the publication "Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system" to train CNNs based on different ResNet architectures to characterize fibrotic and inflammatory lung diseases from sections of lung tissue.
Overview:
| Dataset | Publication architecture | Publication benchmark | Experimental architecture | Experimental results |
|---|---|---|---|---|
| Inflammation | Inception V3 | 80.0% | ResNet34 | 83.3% |
| Fibrosis | Inception V3 | 79.5% | ResNet34 | 80.3% |
Notebooks:
- lung_inflammation_v4_ResNet34.ipynb: Uses a ResNet34 architecture and improves the inflammatory accuracy benchmark from the publication by approx. 3% (trained weights, 42 MB).
- lung_fibrosis_v4_ResNet34.ipynb: Uses a ResNet34 architecture and improves the fibrosis accuracy benchmark from the publication by approx. 0.8% (trained weights, 42 MB).
- lung_fibrosis_v5_ResNet34_Grad-CAM.ipynb: Grad-CAM visualization of classified images.
Deprecated notebooks:
- lung_inflammation_v3_progressive-resizing-ResNet34.ipynb: Uses the trained weights from the ResNet34 from below and increases the image size to 512 ("progressive resizing"). This approach improved the inflammatory accuracy benchmark from the publication by approx. 2%.
- lung_inflammation_v2_from-folder-ResNet34.ipynb: Uses a ResNet34 architecture and slightly improves the inflammatory accuracy benchmark from the publication.
- lung_inflammation_v2_from-folder-ResNet18.ipynb: Uses a ResNet18 architecture and achieves a similar inflammatory accuracy benchmark compared to the publication.
- lung_inflammation_v1_from-df.ipynb: Uses deprecated version of the fastai library.