Plug-and-Play Feature Generation for Few-Shot Medical Image
  Classification
Qianyu Guo Huifang Du Xing Jia Shuyong Gao Yan Teng Haofen Wang Wenqiang Zhang
Few-shot learning (FSL) presents immense potential in enhancing model
generalization and practicality for medical image classification with limited
training data; however, it still faces the challenge of severe overfitting in
classifier training due to distribution bias caused by the scarce training
samples. To address the issue, we propose MedMFG, a flexible and lightweight
plug-and-play method designed to generate sufficient class-distinctive features
from limited samples. Specifically, MedMFG first re-represents the limited
prototypes to assign higher weights for more important information features.
Then, the prototypes are variationally generated into abundant effective
features. Finally, the generated features and prototypes are together to train
a more generalized classifier. Experiments demonstrate that MedMFG outperforms
the previous state-of-the-art methods on cross-domain benchmarks involving the
transition from natural images to medical images, as well as medical images
with different lesions. Notably, our method achieves over 10% performance
improvement compared to several baselines. Fusion experiments further validate
the adaptability of MedMFG, as it seamlessly integrates into various backbones
and baselines, consistently yielding improvements of over 2.9% across all
results.