  Whole slide images (WSIs) pose unique challenges when training deep learning
models. They are very large which makes it necessary to break each image down
into smaller patches for analysis, image features have to be extracted at
multiple scales in order to capture both detail and context, and extreme class
imbalances may exist. Significant progress has been made in the analysis of
these images, thanks largely due to the availability of public annotated
datasets. We postulate, however, that even if a method scores well on a
challenge task, this success may not translate to good performance in a more
clinically relevant workflow. Many datasets consist of image patches which may
suffer from data curation bias; other datasets are only labelled at the whole
slide level and the lack of annotations across an image may mask erroneous
local predictions so long as the final decision is correct. In this paper, we
outline the differences between patch or slide-level classification versus
methods that need to localize or segment cancer accurately across the whole
slide, and we experimentally verify that best practices differ in both cases.
We apply a binary cancer detection network on post neoadjuvant therapy breast
cancer WSIs to find the tumor bed outlining the extent of cancer, a task which
requires sensitivity and precision across the whole slide. We extensively study
multiple design choices and their effects on the outcome, including
architectures and augmentations. Furthermore, we propose a negative data
sampling strategy, which drastically reduces the false positive rate (7% on
slide level) and improves each metric pertinent to our problem, with a 15%
reduction in the error of tumor extent.
