Quantum Annealing Feature Selection on Light-weight Medical Image Datasets

2025-02-26

AI Summary

Classical feature selection algorithms suffer from poor scalability in high-dimensional medical image analysis. Method: This work pioneers the deployment of a lightweight medical image feature selection task—formulated as a “k-out-of-n” combinatorial optimization problem—on commercially available quantum annealing hardware. We model the problem as an Ising optimization with linear penalty terms and integrate subsampling and thresholding strategies to enhance mapping efficiency and hardware compatibility. Feature selection is realized via pixel-wise masking and subsequent image reconstruction. Contribution/Results: Our approach successfully identifies discriminative pixel regions in small-scale medical image reconstruction tasks. Experimental results demonstrate the feasibility of quantum annealing for specific high-dimensional combinatorial optimization problems in medical imaging, offering a novel pathway for deploying quantum machine learning in resource-constrained clinical settings.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Computer Vision: Medical and Biological Imaging

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n. As problem sizes grow, classical approaches struggle to scale efficiently. Quantum computers, particularly quantum annealers, are well-suited for such problems, offering potential advantages in specific formulations. We present a method to solve larger feature selection instances than previously presented on commercial quantum annealers. Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability. The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images. The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks. However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware.

Problem

• Quantum annealing for feature selection
• Enhancing scalability in medical image datasets
• Addressing high-dimensional optimization challenges

Innovation

• Quantum annealing for feature selection
• Linear Ising penalty mechanism
• Subsampling and thresholding techniques