Unsupervised augmentation optimization for few-shot medical image segmentation

• URL: http://arxiv.org/abs/2306.05107v1
• Date: Thu, 8 Jun 2023 11:15:04 GMT
• Title: Unsupervised augmentation optimization for few-shot medical image segmentation
• Authors: Quan Quan, Shang Zhao, Qingsong Yao, Heqin Zhu, S. Kevin Zhou
• Abstract summary: We propose a framework to determine the optimal'' parameters without human annotations. Extensive experiments demonstrate the superiority of our optimized augmentation in boosting few-shot segmentation models. We even achieve a significant improvement for SSL-ALP on the left kidney by 3.39% on the Abd-CT dataset.
• Score: 15.651920380963073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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