Localized Region Contrast for Enhancing Self-supervised Learning in Medical Image Segmentation

被引:3
|
作者
Yan, Xiangyi [1 ]
Naushad, Junayed [1 ,2 ]
You, Chenyu [3 ]
Tang, Hao [1 ]
Sun, Shanlin [1 ]
Han, Kun [1 ]
Ma, Haoyu [1 ]
Duncan, James S. [3 ]
Xie, Xiaohui [1 ]
机构
[1] Univ Calif Irvine, Irvine, CA 92697 USA
[2] Univ Oxford, Oxford, England
[3] Yale Univ, New Haven, CT USA
来源
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023, PT II | 2023年 / 14221卷
关键词
Self-supervised Learning; Contrastive Learning; Semantic Segmentation;
D O I
10.1007/978-3-031-43895-0_44
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recent advancements in self-supervised learning have demonstrated that effective visual representations can be learned from unlabeled images. This has led to increased interest in applying self-supervised learning to the medical domain, where unlabeled images are abundant and labeled images are difficult to obtain. However, most self-supervised learning approaches are modeled as image level discriminative or generative proxy tasks, which may not capture the finer level representations necessary for dense prediction tasks like multi-organ segmentation. In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation. Our approach involves identifying Super-pixels by Felzenszwalb's algorithm and performing local contrastive learning using a novel contrastive sampling loss. Through extensive experiments on three multi-organ segmentation datasets, we demonstrate that integrating LRC to an existing self-supervised method in a limited annotation setting significantly improves segmentation performance. Moreover, we show that LRC can also be applied to fully-supervised pre-training methods to further boost performance.
引用
收藏
页码:468 / 478
页数:11
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