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Related Experiment Video

Updated: Jun 7, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification.

Shichao Du1, Bing Zhu1, Miao Yu1

  • 1Jilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Jilin 136000, People's Republic of China.

Journal of Neural Engineering
|June 5, 2026
PubMed
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This study introduces TriPathNet with Arbiter, a novel deep learning framework for Alzheimer's disease (AD) diagnosis using structural MRI. It enhances diagnostic accuracy and robustness by addressing prediction uncertainty and anatomical inconsistencies.

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a progressive neurodegenerative disorder requiring early diagnosis.
  • Deep learning on structural MRI (sMRI) shows promise for computer-aided AD diagnosis but faces challenges with heterogeneity and uncertainty.
  • Test-time perturbations can cause anatomical inconsistencies, impacting diagnostic stability.

Purpose of the Study:

  • To develop an uncertainty-aware deep learning framework for robust Alzheimer's disease diagnosis using sMRI.
  • To improve the reliability of computer-aided diagnosis by handling sample-specific heterogeneity and prediction uncertainty.

Main Methods:

  • Proposed TriPathNet with Arbiter, an uncertainty-aware multi-path framework utilizing parallel encoding and metric-guided coordination.
Keywords:
Alzheimer’s diseasedecision arbitrationdeep learningmulti-view fusionstructural MRItest-time augmentation

Related Experiment Videos

Last Updated: Jun 7, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

  • Implemented an augmentation-aware arbitration mechanism to correct predictions for high-uncertainty samples during inference.
  • Evaluated performance on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset for AD vs. cognitively normal (CN) classification.

    • Main Results:

    • Achieved high accuracy (98.85%), sensitivity (99.18%), specificity (97.06%), and AUC (99.61%) on the ADNI dataset.
    • Demonstrated stable performance under test-time perturbations, indicating robustness.
    • The framework effectively handles sample-specific heterogeneity and prediction uncertainty.

    Conclusions:

    • TriPathNet with Arbiter offers an accurate and robust solution for sMRI-based computer-aided Alzheimer's disease diagnosis.
    • The proposed method shows significant potential for clinical application in early AD detection.
    • Addressing uncertainty and perturbations is crucial for reliable deep learning in medical imaging.