  Histopathological cancer diagnostics has become more complex, and the
increasing number of biopsies is a challenge for most pathology laboratories.
Thus, development of automatic methods for evaluation of histopathological
cancer sections would be of value. In this study, we used 624 whole slide
images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded
convolutional neural network design, called H2G-Net, for semantic segmentation
of gigapixel histopathological images. The design involves a detection stage
using a patch-wise method, and a refinement stage using a convolutional
autoencoder. To validate the design, we conducted an ablation study to assess
the impact of selected components in the pipeline on tumour segmentation.
Guiding segmentation, using hierarchical sampling and deep heatmap refinement,
proved to be beneficial when segmenting the histopathological images. We found
a significant improvement when using a refinement network for postprocessing
the generated tumour segmentation heatmaps. The overall best design achieved a
Dice score of 0.933 on an independent test set of 90 WSIs. The design
outperformed single-resolution approaches, such as cluster-guided, patch-wise
high-resolution classification using MobileNetV2 (0.872) and a low-resolution
U-Net (0.874). In addition, segmentation on a representative x400 WSI took ~58
seconds, using only the CPU. The findings demonstrate the potential of
utilizing a refinement network to improve patch-wise predictions. The solution
is efficient and does not require overlapping patch inference or ensembling.
Furthermore, we showed that deep neural networks can be trained using a random
sampling scheme that balances on multiple different labels simultaneously,
without the need of storing patches on disk. Future work should involve more
efficient patch generation and sampling, as well as improved clustering.
