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- Title
Deep learning-assisted medical image compression challenges and opportunities: systematic review.
- Authors
Bourai, Nour El Houda; Merouani, Hayet Farida; Djebbar, Akila
- Abstract
Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various methodologies, among which deep learning emerges as a particularly auspicious technique. The captivating attribute of deep learning, characterised by its capacity to glean intricate feature representations from data, has served as a catalyst for pioneering approaches and methodologies spanning a multitude of domains. In the face of the burgeoning exponential growth in digital medical image data, the exigency for adept image compression methodologies has become increasingly pronounced. These methodologies are designed to preserve bandwidth and storage resources, thereby ensuring the seamless and efficient transmission of data within medical applications. The critical nature of medical image compression accentuates the imperative to confront the challenges precipitated by the escalating deluge of medical image data. This review paper undertakes a comprehensive examination of medical image compression, with a predominant focus on sophisticated, research-driven deep learning techniques. It delves into a spectrum of approaches, encompassing the amalgamation of deep learning with conventional compression algorithms and the application of deep learning to enhance compression quality. Additionally, the review endeavours to explicate these fundamental concepts, elucidating their inherent characteristics, merits, and limitations.
- Subjects
IMAGE compression; COMPUTER-assisted image analysis (Medicine); DIAGNOSTIC imaging; DEEP learning; ARTIFICIAL intelligence; DATA transmission systems
- Publication
Neural Computing & Applications, 2024, Vol 36, Issue 17, p10067
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-024-09660-8