A Review on Few-shot Learning for Medical Image Segmentation

Yeong-Joon Kim, Donggoo Kang, Yeongheon Mok, Sunkyu Kwon, Joonki Paik

Abstract: Deep-learning based approach has solved various medical imaging problems successfully. Since the lack of training data issues caused by patient privacy, the few-shot learning method has been studied widely. However, this issue still afflicts model performance even in few-shot learning methods. To solve this issue, it is important to quickly optimize the initial parameter values using a small amount of data. In addition, to utilize small data effectively, it is important to design the objective function for segmentation suitable for GT (Ground Truth) with few-shots. In this paper, we experiment with various algorithms using the MAML (Model Agnostic Meta-Learning) method. And we propose an optimal few-shot semantic segmentation network. The proposed method uses a gradient descent algorithm and optimizer parameter decomposition method to ensure fast convergence with fewer data. Experimental results show high performance and fast convergence using fewer datasets than conventional methods.