About This Special Issue
Few-shot learning is defined as learning models to solve problems from small samples. In recent years, under the trend of training model 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 will cost a lot of manpower.
At present, machine learning 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. Essential to machine learning is to deal with a small dataset or few-shot learning, which aims to develop learning models that can generalize rapidly from a few examples. Though challenging, few-shot learning has gained increasing popularity since inception and has mostly focused on the studies in general deep learning contexts.
This special issue aims at gathering the recent advances and novel contributions from academic researchers and industry practitioners in the vibrant topic of few-shot learning to achieve better development of deep learning methods in the field of smart medicine and human health. In addition, this special issue welcomes relevant researchers to discuss the latest developments in the feasibility of new applications of deep learning methods in healthcare management systems or software.
Potential topics include but are not limited to the following:
- 1)Medical image analysis and small samples
- 2)Medical image segmentation and few-shot learning
- 3)Medical image annotation for few-shot learning
- 4)Feature learning of medical image with high-dimensional small samples
- 5)Disease screening in few-shot learning
- 6)Clinical decision and deep learning
- 7)Personal health data analysis
- 8)Intelligent health management based on data processing and chip technology
Keywords:
- Medical image analysis
- Medical image segmentation
- Few-shot learning
- Deep learning
- Intelligent health management
- Machine learning
