  Deep learning has been widely used to analyze digitized hematoxylin and eosin
(H&E)-stained histopathology whole slide images. Automated cancer segmentation
using deep learning can be used to diagnose malignancy and to find novel
morphological patterns to predict molecular subtypes. To train pixel-wise
cancer segmentation models, manual annotation from pathologists is generally a
bottleneck due to its time-consuming nature. In this paper, we propose Deep
Interactive Learning with a pretrained segmentation model from a different
cancer type to reduce manual annotation time. Instead of annotating all pixels
from cancer and non-cancer regions on giga-pixel whole slide images, an
iterative process of annotating mislabeled regions from a segmentation model
and training/finetuning the model with the additional annotation can reduce the
time. Especially, employing a pretrained segmentation model can further reduce
the time than starting annotation from scratch. We trained an accurate ovarian
cancer segmentation model with a pretrained breast segmentation model by 3.5
hours of manual annotation which achieved intersection-over-union of 0.74,
recall of 0.86, and precision of 0.84. With automatically extracted high-grade
serous ovarian cancer patches, we attempted to train another deep learning
model to predict BRCA mutation. The segmentation model and code have been
released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary.
