  Remote screening of respiratory diseases has been widely studied as a
non-invasive and early instrument for diagnosis purposes, especially in the
pandemic. The respiratory sound classification task has been realized with
numerous deep neural network (DNN) models due to their superior performance.
However, in the high-stake medical domain where decisions can have significant
consequences, it is desirable to develop interpretable models; thus, providing
understandable reasons for physicians and patients. To address the issue, we
propose a prototype learning framework, that jointly generates exemplar samples
for explanation and integrates these samples into a layer of DNNs. The
experimental results indicate that our method outperforms the state-of-the-art
approaches on the largest public respiratory sound database.
