  Cancer detection and classification from gigapixel whole slide images of
stained tissue specimens has recently experienced enormous progress in
computational histopathology. The limitation of available pixel-wise annotated
scans shifted the focus from tumor localization to global slide-level
classification on the basis of (weakly-supervised) multiple-instance learning
despite the clinical importance of local cancer detection. However, the worse
performance of these techniques in comparison to fully supervised methods has
limited their usage until now for diagnostic interventions in domains of
life-threatening diseases such as cancer. In this work, we put the focus back
on tumor localization in form of a patch-level classification task and take up
the setting of so-called coarse annotations, which provide greater training
supervision while remaining feasible from a clinical standpoint. To this end,
we present a novel ensemble method that not only significantly improves the
detection accuracy of metastasis on the open CAMELYON16 data set of sentinel
lymph nodes of breast cancer patients, but also considerably increases its
robustness against noise while training on coarse annotations. Our experiments
show that better results can be achieved with our technique making it
clinically feasible to use for cancer diagnosis and opening a new avenue for
translational and clinical research.
