  In the past few years, there are several researches on Parkinson's disease
(PD) recognition using single-photon emission computed tomography (SPECT)
images with deep learning (DL) approach. However, the DL model's complexity
usually results in difficult model interpretation when used in clinical. Even
though there are multiple interpretation methods available for the DL model,
there is no evidence of which method is suitable for PD recognition
application. This tutorial aims to demonstrate the procedure to choose a
suitable interpretation method for the PD recognition model. We exhibit four
DCNN architectures as an example and introduce six well-known interpretation
methods. Finally, we propose an evaluation method to measure the interpretation
performance and a method to use the interpreted feedback for assisting in model
selection. The evaluation demonstrates that the guided backpropagation and SHAP
interpretation methods are suitable for PD recognition methods in different
aspects. Guided backpropagation has the best ability to show fine-grained
importance, which is proven by the highest Dice coefficient and lowest mean
square error. On the other hand, SHAP can generate a better quality heatmap at
the uptake depletion location, which outperforms other methods in
discriminating the difference between PD and NC subjects. Shortly, the
introduced interpretation methods can contribute to not only the PD recognition
application but also to sensor data processing in an AI Era (interpretable-AI)
as feedback in constructing well-suited deep learning architectures for
specific applications.
