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Related Concept Videos

Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Multimodal Disentangled Variational Autoencoder With Game Theoretic Interpretability for Glioma Grading.

Jianhong Cheng, Min Gao, Jin Liu

    IEEE Journal of Biomedical and Health Informatics
    |July 8, 2021
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    Summary

    This study introduces a novel multimodal disentangled variational autoencoder (MMD-VAE) for enhanced glioma grading using multimodal magnetic resonance imaging (MRI). The MMD-VAE effectively fuses complementary MRI data, significantly improving diagnostic accuracy.

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    Area of Science:

    • Neuroimaging
    • Artificial Intelligence in Medicine
    • Oncology

    Background:

    • Multimodal magnetic resonance imaging (MRI) offers complementary information crucial for accurate glioma grading.
    • Extracting shared and unique data from different MRI modalities for effective fusion remains a challenge.

    Purpose of the Study:

    • To develop a deep learning model for glioma grading by effectively fusing multimodal MRI data.
    • To extract common and distinctive radiomic features for improved diagnostic accuracy.

    Main Methods:

    • A multimodal disentangled variational autoencoder (MMD-VAE) was proposed, utilizing radiomic features from preoperative multimodal MRI.
    • Latent representations were disentangled into common and distinctive components, with specialized loss functions ensuring representation effectiveness.
    • Radiomic features were extracted from regions of interest and quantized.

    Main Results:

    • The MMD-VAE model achieved high predictive performance (AUC: 0.9939) on a public dataset.
    • The model demonstrated strong generalization capabilities (AUC: 0.9611) on a cross-institutional private dataset.
    • SHapley Additive exPlanations (SHAP) were used for feature contribution analysis.

    Conclusions:

    • The proposed MMD-VAE effectively fuses multimodal MRI data for accurate glioma grading.
    • The disentangled representations capture both shared and complementary information, enhancing diagnostic capabilities.
    • This approach can aid radiologists in better understanding gliomas and improving treatment decisions.