Improved multi-site Parkinson's disease classification using neuroimaging data with counterfactual inferenceDownload PDF

Published: 04 Apr 2023, Last Modified: 24 Apr 2023MIDL 2023 PosterReaders: Everyone
Keywords: Domain shift, harmonization, normalizing flow, causality, counterfactual, Parkinson's disease
TL;DR: Counterfactual Inference improves Neuroimaging-based Parkinson's disease classification in multi-site scenarios
Abstract: Deep learning has led to many advances in medical image analysis for various clinical problems. However, most deep learning models are known to be sensitive to differences in the training and test data distributions, which can lead to a decrease in accuracy when applied in real-life scenarios. Thus far, various techniques have been developed to tackle this problem, primarily focusing on harmonizing feature representations from different datasets. Due to the recent increased interest in causal approaches in deep learning, explainable harmonization techniques have gained momentum lately but have not been applied broadly yet. Our study proposes a causal flow-based technique to overcome the problem of different feature distributions in multi-site data used for Parkinson's disease (PD) classification. Feature distributions from six different sites, with a total of 415 subjects (PD: 263, healthy controls: 152), were used for the experiments. A counterfactual approach to answer the question, ``How would brain MRI features appear if they were obtained at a different site?" was developed using a causal normalizing flow. When tested on features from a previously unseen site, the counterfactual-based classifier demonstrated superior performance (weighted f1 = 0.68) compared to a classifier trained on purely observational data (weighted f1 = 0.36) and improved accuracy compared to a harmonization technique typically used in neurological settings (weighted f1 = 0.5). These results show that the proposed technique can effectively correct differences in multi-site feature distributions to facilitate generalizable deep-learning models.
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