  Automatic detection of cancer metastasis from whole slide images (WSIs) is a
crucial step for following patient staging and prognosis. Recent convolutional
neural network based approaches are struggling with the trade-off between
accuracy and computational efficiency due to the difficulty in processing
large-scale gigapixel WSIs. To meet this challenge, we propose a novel
Pyramidal Feature Aggregation ScanNet (PFA-ScanNet) for robust and fast
analysis of breast cancer metastasis. Our method mainly benefits from the
aggregation of extracted local-to-global features with diverse receptive
fields, as well as the proposed synergistic learning for training the main
detector and extra decoder with semantic guidance. Furthermore, a
high-efficiency inference mechanism is designed with dense pooling layers,
which allows dense and fast scanning for gigapixel WSI analysis. As a result,
the proposed PFA-ScanNet achieved the state-of-the-art FROC of 90.2% on the
Camelyon16 dataset, as well as competitive kappa score of 0.905 on the
Camelyon17 leaderboard. In addition, our method shows leading speed advantage
over other methods, about 7.2 min per WSI with a single GPU, making automatic
analysis of breast cancer metastasis more applicable in the clinical usage.
