Mathematics and Machine Learning for Visual Computing in Medicine: Acquisition, Processing, Analysis, Visualization, and Interpretation of Visual Information

Bin Li (), Shixiang Feng, Jinhong Zhang, Guangbin Chen, Shiyang Huang, Sibei Li and Yuxin Zhang
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Bin Li: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Shixiang Feng: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Jinhong Zhang: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Guangbin Chen: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Shiyang Huang: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Sibei Li: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Yuxin Zhang: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China

Mathematics, 2025, vol. 13, issue 11, 1-30

Abstract: Visual computing in medicine involves handling the generation, acquisition, processing, analysis, exploration, visualization, and interpretation of medical visual information. Machine learning has become a prominent tool for data analytics and problem-solving, which is the process of enabling computers to automatically learn from data and obtain certain knowledge, patterns, or input–output relationships. The tasks involving visual computing in medicine often could be transformed into tasks of machine learning. In recent years, there has been a surge in research focusing on machine-learning-based visual computing. However, there are few reviews comprehensively introducing and surveying the systematic implementation of machine-learning-based vision computing in medicine, and in relevant reviews, little attention has been paid to the use of machine learning methods to transform medical visual computing tasks into data-driven learning problems with high-level feature representation, while exploring their effectiveness in key medical applications, such as image-guided surgery. This review paper addresses the above question and surveys fully and systematically the recent advancements, challenges, and future directions regarding machine-learning-based medical visual computing with high-level features. This paper is organized as follows. The fundamentals and paradigm of visual computing in medicine are first concisely introduced. Then, aspects of visual computing in medicine are delved into: (1) acquisition of visual information; (2) processing and analysis of visual information; (3) exploration and interpretation of visual information; and (4) image-guided surgery. In particular, this paper explores machine-learning-based methods and factors for visual computing tasks. Finally, the future prospects are discussed. In conclusion, this literature review on machine learning for visual computing in medicine showcases the diverse applications and advancements in this field.

Keywords: visual computing; image reconstruction; image analysis; segmentation; registration; visualization; augmented reality; machine learning; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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