Few-shot learning is defined as learning models to solve problems with small samples. In recent years, under the trend of training models with big data, machine learning and deep learning have achieved success in many fields. However, in many application scenarios in the real world, there is not a large amount of data or labeled data for model training, and labeling a large number of unlabeled samples costs a lot of manpower.

Currently, computational intelligence technology is widely used in the mining of massive electronic medical records and digital medical images and has made remarkable achievements in the diagnosis and analysis of common diseases. When the current computational intelligence methods deal with small samples, the performance of the model decreases because of the lack of sufficient sample information. If the small sample learning strategy can be integrated into the traditional computational intelligence methods, the computational intelligence technology will be more widely used in the field of medical applications. Few-shot learning is a good choice to solve the small sample problem in medicine. We can try to combine traditional computational intelligence technology with few-shot learning to obtain advanced computational intelligence technology with small sample learning ability.

This Special Issue aims to gather the recent advances and novel contributions from academic researchers and industry practitioners in the vibrant topic of few-shot learning-based advanced computational intelligence technology to achieve better development of deep learning methods in the field of smart medicine and human health. In addition, this Special Issue encourages relevant researchers to discuss the latest developments in the feasibility of new applications of deep learning methods in healthcare management systems or software. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Advanced computational intelligence for medical image analysis and small samples
• Advanced computational intelligence for medical image segmentation and few-shot learning
• Advanced computational intelligence for medical image annotation for few-shot learning
• Advanced computational intelligence for feature learning of medical images with high-dimensional small samples
• Advanced computational intelligence for disease screening in few-shot learning
• Advanced computational intelligence for clinical decision and deep learning
• Advanced computational intelligence for personal health data analysis
• Advanced computational intelligence for intelligent health management based on data processing and chip technology