QUANTUM-INSPIRED APPLICATIONS IN MEDICAL IMAGE PROCESSING: A REVIEW

Abstract

Quantum-inspired computing combines conventional computational techniques with mathematical concepts motivated by quantum mechanics, such as superposition, entanglement-inspired correlations, and wave localization. This review examines recent developments in quantum-inspired methods for medical image denoising, edge detection, segmentation, feature extraction, secure image processing, and multimodal image fusion involving MRI, CT, and ultrasound images. Reported studies indicate that these methods can improve selected measures of image quality, segmentation, feature extraction, and classification under experimental settings. But, the variation of datasets, frameworks for evaluation of methods, baseline algorithms, and dimensions of data complicate the issue of comparing different methods, while low level of external validation does not allow one to conclude whether quantum methods have some advantages with respect to traditional classical and deep learning models.Most of the quantum-influenced methods are implemented on conventional computing systems that give a possibility to use the methods developed using classical image processing methods, and new learning methods, such as few-shot learning and domain adaptation. The current review provides discussion concerning new architectures and evaluation strategies in clinical practice and also potential problems with scalability, readiness of hardware and integration of work processes. One of the main problems is that many studies are conducted on synthetic datasets, and this makes it difficult to estimate the clinical relevance of the methods under consideration.  Overall, this review presents a balanced view of the opportunities, current limitations, and requirements for translating quantum-inspired medical image processing into clinical practice.