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Hybrid AI pipeline classifies Alzheimer's using quantum kernels

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel Hybrid Classical-Quantum (HCQ) pipeline for classifying Alzheimer's disease (AD) using 3D structural MRI scans. The system employs a supervised 3D $\beta$-variational autoencoder (VAE) to extract disease-aware features, which are then encoded into quantum states. These states are used to construct a quantum kernel for a Support Vector Machine (SVM), achieving up to 72.1% accuracy and an AUC of 0.799 on the ADNI-1 dataset. This approach integrates classical deep learning with quantum computing for enhanced diagnostic capabilities in biomedical imaging. AI

IMPACT This hybrid approach could offer a new framework for diagnostic classification in biomedical imaging, potentially improving accuracy and efficiency.

RANK_REASON The cluster describes a novel research paper published on arXiv detailing a new methodology for disease classification.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Hybrid AI pipeline classifies Alzheimer's using quantum kernels

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The cluster describes a novel research paper published on arXiv detailing a new methodology for disease classification.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tia Tiwari, Vamshi Krishna Kancharla, Neelam Sinha ·

    Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $\beta$-VAE and Quantum Kernels

    arXiv:2606.14194v1 Announce Type: cross Abstract: This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structural MRI volumes, where the classical and quantum components are designed to comp…

  2. arXiv cs.LG TIER_1 English(EN) · Neelam Sinha ·

    Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $β$-VAE and Quantum Kernels

    This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structural MRI volumes, where the classical and quantum components are designed to complement each other rather than operate independentl…

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