A Survey on Deep Learning Techniques for Medical Image Fusion

Abstract

Medical Image Fusion is a process that involves combining information from multiple medical images, which is essential for healthcare applications like diagnosis, treatment planning, and image-guided interventions. Deep learning techniques have shown significant promise in medical image fusion by integrating information and effectively capturing data from diverse modalities. Additionally, Data augmentation techniques have come to be an important tool for improving model performance and generalization. The objective of this paper is to give a comprehensive overview of deep learning techniques and data augmentation methods utilized in medical image fusion from 2018 to 2023. The paper covers a variety of topics such as image registration, feature extraction, fusion architectures, data augmentation techniques, and evaluation metrics. The survey also discusses the challenges, limitations, and future directions in the field.