Journal: Current Medical Imaging
Guest editor(s):Dr. Deepak Gupta
Co-Guest Editor(s): Yu-Dong Zhang,Utku Kose

Introduction

Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, which ultimately improves patient care when paired with skilled clinicians. Creating a new quantum machine-learning algorithm tailored to biomedical data is difficult and urgent. There is a critical need for advanced data mining techniques in the healthcare and biological sciences, which have become data-intensive professions. High dimensionality, class imbalance, and a lack of samples are only a few of the difficulties that arise while analyzing biomedical data. Although there have been some encouraging findings from the existing study in this sector, further investigation into the following areas is necessary. In order to increase predicted performance and interpretation and examine large-scale data in the biomedical sciences, it is necessary to investigate innovative feature selection approaches. The aim of this special issue is to compile state-of-the-art research (from both academia and industry) on cutting-edge quantum machine-learning techniques for solving problems with complicated biological data. The challenges of feature selection, class imbalance, and data fusion in the context of biomedical and quantum machine-learning applications will be discussed in depth. Medical professionals who have access to valuable data but aren't proficient in quantum machine learning will be drawn to this field. The topics relevant to the special issue include (but are not limited to) the following topics: • Quantum machine learning for medical image analysis • Quantum machine learning in bioinformatics • Computer-aided detection and diagnosis • Pattern recognition for imaging and genomics • Big data analytics on biomedical applications • Pattern recognition for imaging and genomics • Classification and optimization algorithms in quantum machine learning and applications • Quantum machine learning methods applied to biomedical data • Data anonymization methods for biomedical data • Information Retrieval of Medical Images • Large Datasets and Big Data Analytics on biomedical and healthcare applications • Enhancement and restoration of medical data using quantum processing

Keywords

Quantum Computing, Machine Learning, Deep Learning, Medical Data & imaging

Sub-topics