Quantum-classical convolutional neural networks in radiological image classification

TL;DR

This paper explores hybrid quantum-classical convolutional neural networks for medical image classification, demonstrating their comparable performance to classical models and highlighting potential benefits in data-limited scenarios.

Contribution

It introduces various hybrid quantum-classical CNN architectures and evaluates their performance on medical imaging data, showing promising results for quantum approaches in healthcare.

Findings

Quantum-classical CNNs perform similarly to classical CNNs on medical imaging tasks.

Quantum models may offer advantages in training with limited data.

Encourages further research into quantum machine learning for medical applications.

Abstract

Quantum machine learning is receiving significant attention currently, but its usefulness in comparison to classical machine learning techniques for practical applications remains unclear. However, there are indications that certain quantum machine learning algorithms might result in improved training capabilities with respect to their classical counterparts -- which might be particularly beneficial in situations with little training data available. Such situations naturally arise in medical classification tasks. Within this paper, different hybrid quantum-classical convolutional neural networks (QCCNN) with varying quantum circuit designs and encoding techniques are proposed. They are applied to two- and three-dimensional medical imaging data, e.g. featuring different, potentially malign, lesions in computed tomography scans. The performance of these QCCNNs is already similar to the one of their classical counterparts -- therefore encouraging further studies towards the direction of applying these algorithms within medical imaging tasks.