Publication: Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial
Issued Date
2021-10-15
Resource Type
ISSN
15581748
1530437X
1530437X
Other identifier(s)
2-s2.0-85105868509
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Mahidol University
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SCOPUS
Bibliographic Citation
IEEE Sensors Journal. Vol.21, No.20 (2021), 22304-22316
Suggested Citation
Theerasarn Pianpanit, Sermkiat Lolak, Phattarapong Sawangjai, Thapanun Sudhawiyangkul, Theerawit Wilaiprasitporn Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial. IEEE Sensors Journal. Vol.21, No.20 (2021), 22304-22316. doi:10.1109/JSEN.2021.3077949 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76925
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Title
Parkinson's Disease Recognition Using SPECT Image and Interpretable AI: A Tutorial
Abstract
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 difficultmodel 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 interpretationmethod for the PD recogni-tion model. We exhibit four DCNN architectures as an example and introduce six well-known interpretationmethods. 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 interpretationmethods 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.