Advancing Low-Rank and Local Low-Rank Matrix Approximation in Medical Imaging: A Systematic Literature Review and Future Directions

arXiv:2402.14045v1 Announce Type: cross
Abstract: The large volume and complexity of medical imaging datasets are bottlenecks for storage, transmission, and processing. To tackle these challenges, the application of low-rank matrix approximation (LRMA) and its derivative, local LRMA (LLRMA) has demonstrated potential.
This paper conducts a systematic literature review to showcase works applying LRMA and LLRMA in medical imaging. A detailed analysis of the literature identifies LRMA and LLRMA methods applied to various imaging modalities. This paper addresses the challenges and …

abstract application approximation arxiv bottlenecks challenges complexity cs.cv cs.lg datasets eess.iv future imaging literature low matrix medical medical imaging paper processing review storage type