Deep Hash with Optimal Transport-Based Domain Adaptation for Multisite MRI Retrieval

Abstract

The Internet of Things has a wide range of applications in the medical field. Due to the heterogeneity of medical data generated by different hospitals, it is very important to analyze and integrate data from different institutions. Functional magnetic resonance imaging (fMRI) is widely used in clinical medicine and cognitive neuroscience, while resting-state fMRI (rs-fMRI) can help reveal functional biomarkers of neurological disorders for computer-assisted clinical diagnosis and prognosis. Recently, how to retrieve similar images or case histories from large-scale medical image repositories acquired from multiple sites has attracted widespread attention in the field of intelligent diagnosis of diseases. Although using multisite data effectively helps increase the sample size, it also inevitably introduces the problem of data heterogeneity across sites. To address this problem, we propose a multisite fMRI retrieval (MSFR) method that uses a deep hashing approach and an optimal transport-based domain adaptation strategy to mitigate multisite data heterogeneity for accurate fMRI search. Specifically, for a given target domain site and multiple source domain sites, our approach uses a deep neural network to map the source and target domain data into the latent feature space and minimize their Wasserstein distance to reduce their distribution differences. We then use the source domain data to learn high-quality hash code through a global similarity metric, thereby improving the performance of cross-site fMRI retrieval. We evaluated our method on the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results show the effectiveness of our method in resting-state fMRI retrieval.