临床实践中应用自动化的深度学习分类器有潜力简化诊断流程并提高诊断准确性，但这些分类器的接受度取决于它们的准确性和可解释性。一般来说，准确的深度学习分类器提供很少的模型可解释性，而可解释的模型则没有竞争力的分类准确性。本文介绍了一种新的深度学习诊断框架，称为InterNRL，旨在实现高准确性和可解释性。InterNRL由一个学生-教师框架组成，其中学生模型是一个可解释的基于原型的分类器（ProtoPNet），教师是一个准确的全局图像分类器（GlobalNet）。这两个分类器是通过一种新颖的互补学习范式相互优化的，学生ProtoPNet从教师GlobalNet生成的最佳伪标签中学习，而GlobalNet则从ProtoPNet的分类性能和伪标签中学习。这种互补学习范式使InterNRL能够在全监督学习和半监督学习场景下灵活优化，在乳腺癌和视网膜疾病诊断任务的两种场景下，达到了最先进的分类性能。此外，依靠弱标记的训练图像，InterNRL在乳腺癌定位和脑肿瘤分割方面也取得了优于其他竞争方法的结果。

The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, while interpretable models do not have competitive classification accuracy. In this paper, we introduce a new deep-learning diagnosis framework, called InterNRL, that is designed to be highly accurate and interpretable. InterNRL consists of a student-teacher framework, where the student model is an interpretable prototype-based classifier (ProtoPNet) and the teacher is an accurate global image classifier (GlobalNet). The two classifiers are mutually optimised with a novel reciprocal learning paradigm in which the student ProtoPNet learns from optimal pseudo labels produced by the teacher GlobalNet, while GlobalNet learns from ProtoPNet's classification performance and pseudo labels. This reciprocal learning paradigm enables InterNRL to be flexibly optimised under both fully- and semi-supervised learning scenarios, reaching state-of-the-art classification performance in both scenarios for the tasks of breast cancer and retinal disease diagnosis. Moreover, relying on weakly-labelled training images, InterNRL also achieves superior breast cancer localisation and brain tumour segmentation results than other competing methods.