The augmentation parameters matter to few-shot semantic segmentation since
they directly affect the training outcome by feeding the networks with varying
perturbated samples. However, searching optimal augmentation parameters for
few-shot segmentation models without annotations is a challenge that current
methods fail to address. In this paper, we first propose a framework to
determine the ``optimal'' parameters without human annotations by solving a
distribution-matching problem between the intra-instance and intra-class
similarity distribution, with the intra-instance similarity describing the
similarity between the original sample of a particular anatomy and its
augmented ones and the intra-class similarity representing the similarity
between the selected sample and the others in the same class. Extensive
experiments demonstrate the superiority of our optimized augmentation in
boosting few-shot segmentation models. We greatly improve the top competing
method by 1.27\% and 1.11\% on Abd-MRI and Abd-CT datasets, respectively, and
even achieve a significant improvement for SSL-ALP on the left kidney by 3.39\%
on the Abd-CT dataset.

本研究提出了一种在没有人工注释的情况下通过解决部位内相似度和类内相似度之间分配匹配问题来确定最佳数据增强参数的优化框架，进而在医学图像分割中证明了其对于提升 few-shot 模型性能的有效性。