  Multi-class colorectal tissue classification is a challenging problem that is
typically addressed in a setting, where it is assumed that ample amounts of
training data is available. However, manual annotation of fine-grained
colorectal tissue samples of multiple classes, especially the rare ones like
stromal tumor and anal cancer is laborious and expensive. To address this, we
propose a knowledge distillation-based approach, named KD-CTCNet, that
effectively captures local texture information from few tissue samples, through
a distillation loss, to improve the standard CNN features. The resulting
enriched feature representation achieves improved classification performance
specifically in low data regimes. Extensive experiments on two public datasets
of colorectal tissues reveal the merits of the proposed contributions, with a
consistent gain achieved over different approaches across low data settings.
The code and models are publicly available on GitHub.
