  Deep learning shows great potential for the domain of digital pathology. An
automated digital pathology system could serve as a second reader, perform
initial triage in large screening studies, or assist in reporting. However, it
is expensive to exhaustively annotate large histology image databases, since
medical specialists are a scarce resource. In this paper, we apply the
semi-supervised teacher-student knowledge distillation technique proposed by
Yalniz et al. (2019) to the task of quantifying prognostic features in
colorectal cancer. We obtain accuracy improvements through extending this
approach to a chain of students, where each student's predictions are used to
train the next student i.e. the student becomes the teacher. Using the chain
approach, and only 0.5% labelled data (the remaining 99.5% in the unlabelled
pool), we match the accuracy of training on 100% labelled data. At lower
percentages of labelled data, similar gains in accuracy are seen, allowing some
recovery of accuracy even from a poor initial choice of labelled training set.
In conclusion, this approach shows promise for reducing the annotation burden,
thus increasing the affordability of automated digital pathology systems.
