Quantum-classical convolutional neural networks in radiological image classification

Matic, Andrea; Monnet, Maureen; Lorenz, Jeanette Miriam; Schachtner, Balthasar und Messerer, Thomas (2022): Quantum-classical convolutional neural networks in radiological image classification. 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, 18-23 September 2022. In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Piscataway, NJ, USA: IEEE. S. 56-66

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Medizin > Klinikum der LMU München > Klinik und Poliklinik für Radiologie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Ort: Piscataway, NJ, USA
• Sprache: Englisch