Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial

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 cl...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Pianpanit, Theerasarn, Lolak, Sermkiat, Sawangjai, Phattarapong, Sudhawiyangkul, Thapanun, Wilaiprasitporn, Theerawit
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation
Comparative studies
Computed tomography
Convolution
Deep learning
Diagnosis
Ground truth
Machine learning
Medical imaging
Model accuracy
Nuclei
Parkinson's disease
Parkinsons disease
Photon emission
Three dimensional models
Tomography
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1908.11199