  We provide in this paper a comprehensive comparison of various transfer
learning strategies and deep learning architectures for computer-aided
classification of adult-type diffuse gliomas. We evaluate the generalizability
of out-of-domain ImageNet representations for a target domain of
histopathological images, and study the impact of in-domain adaptation using
self-supervised and multi-task learning approaches for pretraining the models
using the medium-to-large scale datasets of histopathological images. A
semi-supervised learning approach is furthermore proposed, where the fine-tuned
models are utilized to predict the labels of unannotated regions of the whole
slide images (WSI). The models are subsequently retrained using the
ground-truth labels and weak labels determined in the previous step, providing
superior performance in comparison to standard in-domain transfer learning with
balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the
pathologist's efforts for annotation. Finally, we provide a visualization tool
working at WSI level which generates heatmaps that highlight tumor areas; thus,
providing insights to pathologists concerning the most informative parts of the
WSI.
