Curriculum label distribution learning for imbalanced medical image segmentation
Curriculum label distribution learning for imbalanced medical image segmentation
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DOI：
10.1016/j.media.2023.102911
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发表时间：
2023-07
影响因子：
10.9
通讯作者：
Xiangyu Li;Gongning Luo;Wei Wang;Kuanquan Wang;Shuo Li
中科院分区：
文献类型：
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作者：
Xiangyu Li;Gongning Luo;Wei Wang;Kuanquan Wang;Shuo Li
标签分布学习（LDL）有可能解决语义分割任务中的边界模糊问题。然而，现有的基于LDL的分割方法遭受严重的标签分布不平衡：模糊的标签分布包含一小部分的数据，而明确的标签分布占据了大部分的数据。不平衡的标签分布会导致模型偏向的分布学习，并使准确预测模糊像素变得具有挑战性。在本文中，我们提出了一个课程标签分布学习（CLDL）框架，以解决上述数据不平衡的问题，通过执行一个新的面向任务的课程学习策略。首先，提出了区域标签分布学习（R-LDL），以构造更均衡的标签分布，改善不均衡的模型学习。其次，提出了一种新的学习课程（TCL），通过将分割任务分解为多个标签分布估计任务，使基于LDL的分割中的学习从容易到困难。第三，提出了先验感知模块（PPM），有效地连接容易和困难的学习阶段的基础上产生的先验知识。基于均衡的标签分布结构和先验感知，CLDL有效地引导了基于课程学习的学习目标，显著改善了学习的不均衡性。我们使用公开可用的BRATS 2018和MM-WHS 2017数据集评估了拟议的CLDL。实验结果表明，我们的方法显着提高不同的分割指标相比，许多国家的最先进的方法。代码将可用。1
Label distribution learning (LDL) has the potential to resolve boundary ambiguity in semantic segmentation tasks. However, existing LDL-based segmentation methods suffer from severe label distribution imbalance: the ambiguous label distributions contain a small fraction of the data, while the unambiguous label distributions occupy the majority of the data. The imbalanced label distributions induce model-biased distribution learning and make it challenging to accurately predict ambiguous pixels. In this paper, we propose a curriculum label distribution learning (CLDL) framework to address the above data imbalance problem by performing a novel task-oriented curriculum learning strategy. Firstly, the region label distribution learning (R-LDL) is proposed to construct more balanced label distributions and improves the imbalanced model learning. Secondly, a novel learning curriculum (TCL) is proposed to enable easy-to-hard learning in LDL-based segmentation by decomposing the segmentation task into multiple label distribution estimation tasks. Thirdly, the prior perceiving module (PPM) is proposed to effectively connect easy and hard learning stages based on the priors generated from easier stages. Benefiting from the balanced label distribution construction and prior perception, the proposed CLDL effectively conducts a curriculum learning-based LDL and significantly improves the imbalanced learning. We evaluated the proposed CLDL using the publicly available BRATS2018 and MM-WHS2017 datasets. The experimental results demonstrate that our method significantly improves different segmentation metrics compared to many state-of-the-art methods. The code will be available.1