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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Related Experiment Video

Updated: Jul 9, 2025

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers
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EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning.

Wonsik Jung, Eunjin Jeon, Eunsong Kang

    IEEE Transactions on Medical Imaging
    |November 28, 2023
    PubMed
    Summary

    This study introduces a new deep learning framework for diagnosing autism spectrum disorder (ASD) using resting-state functional magnetic resonance imaging (rs-fMRI). The method enhances diagnostic accuracy by analyzing complex brain connectivity patterns and identifying key brain regions.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for diagnosing brain disorders like autism spectrum disorder (ASD).
    • Current deep learning models using rs-fMRI functional connectivity (FC) have limitations, including insufficient data, lack of individual patient consideration, and poor explainability.
    • Existing methods often rely on linear, low-order FC, failing to capture complex, non-linear brain interactions.

    Purpose of the Study:

    • To address limitations in current deep learning models for ASD diagnosis using rs-fMRI.
    • To propose a novel explainability-guided region of interest (ROI) selection (EAG-RS) framework.
    • To identify non-linear, high-order functional associations and class-discriminative brain regions for improved ASD identification.

    Main Methods:

    • The EAG-RS framework involves inter-regional relation learning using random seed-based network masking to capture non-linear brain interactions.
    • Explainable artificial intelligence techniques are employed to estimate connection-wise relevance scores, exploring high-order functional relations.
    • Non-linear, high-order FC is used for diagnosis-informative ROI selection and classifier learning to identify ASD.

    Main Results:

    • The proposed EAG-RS framework demonstrated superior performance compared to existing methods on the Autism Brain Imaging Database Exchange (ABIDE) dataset.
    • The method achieved higher accuracy across various evaluation metrics, indicating its effectiveness in ASD identification.
    • Qualitative analysis of selected ROIs revealed potential links to known ASD subtypes, aligning with neuroscientific findings.

    Conclusions:

    • The EAG-RS framework offers a significant advancement in diagnosing ASD by effectively utilizing non-linear, high-order functional connectivity from rs-fMRI.
    • The explainability component provides insights into the decision-making process, enhancing trust and understanding of the model's predictions.
    • This approach holds promise for more personalized and accurate diagnosis of ASD, potentially identifying distinct patient subgroups.