  Pathologists find tedious to examine the status of the sentinel lymph node on
a large number of pathological scans. The examination process of such lymph
node which encompasses metastasized cancer cells is histopathologically
organized. However, the task of finding metastatic tissues is gradual which is
often challenging. In this work, we present our deep convolutional neural
network based model validated on PatchCamelyon (PCam) benchmark dataset for
fundamental machine learning research in histopathology diagnosis. We find that
our proposed model trained with a semi-supervised learning approach by using
pseudo labels on PCam-level significantly leads to better performances to
strong CNN baseline on the AUC metric.
