# Quantum Annealing Feature Selection on Light-weight Medical Image Datasets

arXiv: https://arxiv.org/abs/2502.19201 · HTML: https://mlsanity.com/paper/2502.19201/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Feature Selection on Quantum Computers](https://mlsanity.com/paper/2203.13261/) (2022)
- [Feature Selection for Recommender Systems with Quantum Computing](https://mlsanity.com/paper/2110.05089/) (2021)
- [Quantum Annealing for Single Image Super-Resolution](https://mlsanity.com/paper/2304.08924/) (2023)
- [Highly accurate quantum optimization algorithm for CT image reconstructions based on sinogram patterns](https://mlsanity.com/paper/2207.02448/) (2022)
- [Quantum optimization algorithms for CT image segmentation from X-ray data](https://mlsanity.com/paper/2306.05522/) (2023)
- [A Genetic Quantum Annealing Algorithm](https://mlsanity.com/paper/2209.07455/) (2022)
- [Hybrid adiabatic quantum computing for tomographic image reconstruction -- opportunities and limitations](https://mlsanity.com/paper/2212.01312/) (2022)

## Abstract (arXiv)

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.