Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients

Shi, Changwei; Xin, Xianwei; Zhang, Jiacai

doi:10.1016/j.cmpb.2021.106615

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Multi-task learning is superior to single-task learning in classification tasks [ 75 ]. Some studies also demonstrate improvements over various feature-based methods when using their proposed methods [ 57 , 60 , 61 , 62 , 69 , 72 ]. It is important to note that studies that perform different regularisation techniques are not counted as comparative analyses in this study.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

et al. 2023

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Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

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Section: Discussionmentioning

confidence: 99%

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

et al. 2023

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“…The proposed HMSDA+LOSMFS was also compared with 8 state-of-the-art multi-site and multimodality methods: including traditional machine learning methods (1) Dempster Shafer DA-optimal transport (DS-OT) [40], (2) optimal transport-based pyramid graph kernel (OTPGK) [41]; and deep learning methods (3) Convolutional neural network (CNN) [42], (4) denoising autoencoder (DAE) [4],…”

Section: Multi-modality Single-site Classificationmentioning

confidence: 99%

Joint multi-site domain adaptation and multi-modality feature selection for the diagnosis of psychiatric disorders

Ji,

Silva,

Adali

et al. 2024

Preprint

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Identifying biomarkers for computer-aided diagnosis (CAD) is crucial for early intervention of psychiatric disorders. Multi-site data have been utilized to increase the sample size and improve statistical power, while multi-modality classification offers significant advantages over traditional single-modality based approaches for diagnosing psychiatric disorders. However, inter-site heterogeneity and intra-modality heterogeneity present challenges to multi-site and multi-modality based classification. In this paper, brain functional and structural networks (BFNs/BSNs) from multiple sites were constructed to establish a joint multi-site multi-modality framework for psychiatric diagnosis. To do this we developed a hypergraph based multi-source domain adaptation (HMSDA) which allows us to transform source domain subjects into a target domain. A local ordinal structure based multi-task feature selection (LOSMFS) approach was developed by integrating the transformed functional and structural connections (FCs/SCs). The effectiveness of our method was validated by evaluating diagnosis of both schizophrenia (SZ) and autism spectrum disorder (ASD). The proposed method obtained accuracies of 92.2%±2.22% and 84.8%±2.68% for the diagnosis of SZ and ASD, respectively. We also compared with 6 domain adaptation (DA), 10 multi-modality feature selection, and 8 multi-site and multi-modality methods. Results showed the proposed HMSDA + LOSMFS effectively integrates multi-site and multi-modality data to enhance psychiatric diagnosis and identify disorder-specific diagnostic brain connections.

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