# Quantum-classical convolutional neural networks in radiological image classification

arXiv: https://arxiv.org/abs/2204.12390 · HTML: https://mlsanity.com/paper/2204.12390/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 26 Apr 2022.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Variational Quanvolutional Neural Networks with enhanced image encoding](https://mlsanity.com/paper/2106.07327/) (2021)
- [Comparing concepts of quantum and classical neural network models for image classification task](https://mlsanity.com/paper/2108.08875/) (2021)

## Built on by (most cited first, 15 of 15)

- [Incremental Data-Uploading for Full-Quantum Classification](https://mlsanity.com/paper/2205.03057/) (2022)
- [Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images](https://mlsanity.com/paper/2411.19276/) (2024)
- [Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset](https://mlsanity.com/paper/2406.06307/) (2024)
- [Generalization Bounds in Hybrid Quantum-Classical Machine Learning Models](https://mlsanity.com/paper/2504.08456/) (2025)
- [Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy](https://mlsanity.com/paper/2508.00768/) (2025)
- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)
- [Satellite image classification with neural quantum kernels](https://mlsanity.com/paper/2409.20356/) (2024)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Formal Verification of Variational Quantum Circuits](https://mlsanity.com/paper/2507.10635/) (2025)
- [The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment](https://mlsanity.com/paper/2601.04732/) (2026)
- [Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices](https://mlsanity.com/paper/2605.22097/) (2026)
- [Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?](https://mlsanity.com/paper/2307.00080/) (2023)
- [Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks](https://mlsanity.com/paper/2410.05777/) (2024)
- [Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI](https://mlsanity.com/paper/2507.11401/) (2025)
- [Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder](https://mlsanity.com/paper/2606.27411/) (2026)

## Abstract (arXiv)

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.