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.

翻译：多类别结直肠组织分类是一个具有挑战性的问题，通常需要在假设拥有充足训练数据的场景下处理。然而，对多个类别的细粒度结直肠组织样本（尤其是间质肿瘤和肛门癌等罕见类别）进行人工标注既费时又昂贵。为解决这一问题，我们提出了一种基于知识蒸馏的方法KD-CTCNet，通过蒸馏损失从少量组织样本中有效捕获局部纹理信息，以改进标准CNN特征。由此产生的增强特征表示在低数据场景下实现了更优的分类性能。在两个公开结直肠组织数据集上的大量实验揭示了所提贡献的优势，在低数据设置下不同方法均取得了持续的性能提升。代码与模型已在GitHub上公开。