Interpreting deep learning prediction of the Parkinson's disease diagnosis from SPECT imaging

Parkinson's disease (PD) diagnosis mainly relies on the visual and semi-quantitative analysis of medical imaging using single-photon emission computed tomography (SPECT) with 123I-Ioflupane (DaTSCAN). Deep learning approach has the benefits over other machine learning methods because the model does not rely on feature extraction. However, the complexity of the deep learning model usually results in difficulty of interpretation of the model when uses in clinical. Several interpretation methods were created for this approach to show the attention map which reveals important features of the input data, giving the model interpretability. However, it is still unclear whether these methods can be applied to explain PD diagnosis or not. In this paper, four different models of the deep learning approach based on the 3-dimensional convolution neural network (3D-CNN) of well-established architectures have been trained. All the models give high classification performance of PD diagnosis with accuracy up to 95-96\%. These four models have been used to evaluate the interpretation performance of six well-known interpretation methods. In general, radiologists interpret SPECT images for a healthy subject by confirming a homogeneous symmetrical comma type shape of the I123-Ioflupane uptake in the striatal nuclei. Any other shape is interpreted as abnormal. To evaluate the interpretation performance, the segmented striatal nuclei of the SPECT images are chosen to be the ground truth. {\Blue Guided backpropagation which is one of the interpretation methods shows the best performance among all other methods. Guided backpropagation has the best performance to generate the attention map that focuses on the location of striatal nuclei. By using the result from guided backpropagation, 3D CNN architecture that has the highest classification and interpretation performance can be chosen for SPECT diagnosis.