Unsupervised augmentation optimization for few-shot medical image
  segmentation
Quan Quan Shang Zhao Qingsong Yao Heqin Zhu S. Kevin Zhou
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.