  Lymph node metastasis is one of the most significant diagnostic indicators in
breast cancer, which is traditionally observed under the microscope by
pathologists. In recent years, computerized histology diagnosis has become one
of the most rapidly expanding fields in medical image computing, which
alleviates pathologists' workload and reduces misdiagnosis rate. However,
automatic detection of lymph node metastases from whole slide images remains a
challenging problem, due to the large-scale data with enormous resolutions and
existence of hard mimics. In this paper, we propose a novel framework by
leveraging fully convolutional networks for efficient inference to meet the
speed requirement for clinical practice, while reconstructing dense predictions
under different offsets for ensuring accurate detection on both micro- and
macro-metastases. Incorporating with the strategies of asynchronous sample
prefetching and hard negative mining, the network can be effectively trained.
Extensive experiments on the benchmark dataset of 2016 Camelyon Grand Challenge
corroborated the efficacy of our method. Compared with the state-of-the-art
methods, our method achieved superior performance with a faster speed on the
tumor localization task and surpassed human performance on the WSI
classification task.
