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arXiv · 2003.08797

Teacher-Student chain for efficient semi-supervised histology image classification

Shayne Shaw·Maciej Pajak·Aneta Lisowska·Sotirios A Tsaftaris·Alison Q O'Neil
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Abstract

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.

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Shayne Shaw, Maciej Pajak, Aneta Lisowska, Sotirios A Tsaftaris, Alison Q O'Neil. 2020-03-20. Teacher-Student chain for efficient semi-supervised histology image classification. https://arxiv.org/abs/2003.08797

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