Review on the Application of Machine Learning in Medical Image

Abstract

With the advancement of imaging technologies, medical image processing has become increasingly crucial in the medical field. However, traditional methods have limitations when dealing with complex medical data. Machine learning, especially deep learning, has gradually become the core in this area. For example, convolutional neural networks have achieved remarkable results in image classification and lesion detection, and generative adversarial networks and self-supervised learning have alleviated problems such as data scarcity. Chinese researchers have also made numerous achievements. Nevertheless, machine learning in medical image processing still faces challenges, including data heterogeneity, poor model interpretability, and difficulties in privacy protection. This review systematically summarizes the application progress of machine learning in medical image processing. It elaborates on the machine learning methods and application cases in tasks such as image classification, segmentation, detection, registration, and enhancement. It also analyzes the challenges in terms of data, models, and practical applications, and proposes future directions such as self-supervised learning and few-shot learning, multi-modal learning and fusion, interpretability and model transparency, privacy protection and federated learning. The aim is to facilitate the development of machine learning in medical image processing technologies, improve the accuracy and efficiency of clinical diagnosis, and provide better solutions for the medical industry.