Extracting Biomarkers from Hematoxylin- Eosin Stained Histopathological Images of Lung Cancer using Deep Learning
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2019-09-10
Language
en
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Abstract
In this thesis the technique of deep learning was applied to the eld of digital pathology, more
speci cally lung cancer, to extract several di erent biomarkers. Tertiary 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 di erent held-out test set on the object level was 0.640
and 0.500 for TLS and GC respectively.
Treatment di ers per growth pattern of lung adenocarcinoma and variability between pathologists
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 generative
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.
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Faculteit der Sociale Wetenschappen