In this thesis the technique of deep learning was applied to the field of digital pathology, more specifically lung cancer, to extract several different biomarkers. Tertairy lymphoid structures (TLS) have been found to indicate a positive patient prognosis, especially in combination with germinal centers (GC). Therefore, a VGG16-like network was trained to detect TLS and GC in histopathological slides of lung squamous cell carcinoma with F1 scores on the pixel level of 0.922 and 0.802 respectively. Performance on a different held-out test set on the object level was 0.640 and 0.500 for TLS and GC respectively.
Treatment differs per growth pattern of lung adenocarcinoma and variability between pathol ogists in the assessment of lung adenocarcinoma exists. Therefore, a similar VGG16-like network was trained to segment growth patterns of adenocarcinoma in slides of lung tissue with F1 scores on the pixel level of 0.891, 0.524, 0.812 and 0.954 for solid adenocarcinoma, acinar adenocarcinoma, micropapillary adenocarcinoma and non-tumor tissue respectively.
Because the previous system was only trained on sparsely annotated data and consequently did not encounter neighbouring growth patterns of lung adenocarcinoma, a method with genera tive adversarial networks to generate fake densely annotated realistic looking image patches from sparsely annotated data was examined and a comparison between three types of models was made.