  The interpretability of neural networks has recently received extensive
attention. Previous prototype-based explainable networks involved prototype
activation in both reasoning and interpretation processes, requiring specific
explainable structures for the prototype, thus making the network less accurate
as it gains interpretability. Therefore, the decoupling prototypical network
(DProtoNet) was proposed to avoid this problem. This new model contains
encoder, inference, and interpretation modules. As regards the encoder module,
unrestricted feature masks were presented to generate expressive features and
prototypes. Regarding the inference module, a multi-image prototype learning
method was introduced to update prototypes so that the network can learn
generalized prototypes. Finally, concerning the interpretation module, a
multiple dynamic masks (MDM) decoder was suggested to explain the neural
network, which generates heatmaps using the consistent activation of the
original image and mask image at the detection nodes of the network. It
decouples the inference and interpretation modules of a prototype-based network
by avoiding the use of prototype activation to explain the network's decisions
in order to simultaneously improve the accuracy and interpretability of the
neural network. The multiple public general and medical datasets were tested,
and the results confirmed that our method could achieve a 5% improvement in
accuracy and state-of-the-art interpretability compared with previous methods.
