A classification algorithm based on improved meta learning and transfer learning for few‐shot medical images

Abstract

Abstract At present, medical image classification algorithm plays an important role in clinical diagnosis. However, due to the scarcity of data labels, small sample size, uneven distribution, and poor domain generalization, many algorithms still have limitations. Therefore, a deep learning training network for disease classification and recognition of multimodal few‐shot medical images are proposed, trying to solve the above problems and limitations. The network is based on the idea of meta‐learning for training. Specifically, the technology of transfer learning and few‐shot learning are used. In the process of building and improving the network structure, the multi‐source domain generalization method, which performs well in the field of person re‐identification, is combined. Finally, the applicability and effectiveness of the model are verified by using Grad‐CAM tool. The experiments show that the accuracy of classification and recognition of the model is better than the advanced model in this field. The concerned areas of model classification are similar or the same as the manually labelled areas. It is of far‐reaching significance to improve the efficiency of future clinical auxiliary diagnosis and patient diversion, as well as to promote the development of the Wise Information Technology of Med in the future.

Keywords

• Domain generalization
• Few‐shot learning
• Medical image classification
• Meta learning
• Transfer learning