Li Xiangyu, Liang Xinjie, Luo Gongning, Wang Wei, Wang Kuanquan, Li Shuo
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
Med Image Anal. 2023 Dec;90:102944. doi: 10.1016/j.media.2023.102944. Epub 2023 Sep 3.
In this work, we address the task of tumor cellularity (TC) estimation with a novel framework based on the label distribution learning (LDL) paradigm. We propose a self-ensemble label distribution learning framework (SLDL) to resolve the challenges of existing LDL-based methods, including difficulties for inter-rater ambiguity exploitation, proper and flexible label distribution generation, and accurate TC value recovery. The proposed SLDL makes four main contributions which have been demonstrated to be quite effective in numerous experiments. First, we propose an expertness-aware conditional VAE for diversified single-rater modeling and an attention-based multi-rater fusion strategy that enables effective inter-rater ambiguity exploitation. Second, we propose a template-based label distribution generation method that is tailored for the TC estimation task and constructs label distributions based on the annotation priors. Third, we propose a novel restricted distribution loss, significantly improving the TC value estimation by effectively regularizing the learning with unimodal loss and regression loss. Fourth, to the best of our knowledge, we are the first to simultaneously leverage inter-rater and intra-rater variability to address the label ambiguity issue in the breast tumor cellularity estimation tasks. The experimental results on the public BreastPathQ dataset demonstrate that the SLDL outperforms the existing methods by a large margin and achieves new state-of-the-art results in the TC estimation task. The code will be available from https://github.com/PerceptionComputingLab/ULTRA.
在这项工作中，我们使用基于标签分布学习（LDL）范式的新型框架来解决肿瘤细胞密度（TC）估计任务。我们提出了一种自集成标签分布学习框架（SLDL），以解决现有基于LDL的方法所面临的挑战，包括难以利用评分者间的模糊性、生成合适且灵活的标签分布以及准确恢复TC值。所提出的SLDL做出了四项主要贡献，这些贡献在众多实验中已被证明非常有效。首先，我们提出了一种用于多样化单评分者建模的专家感知条件变分自编码器（VAE）和一种基于注意力的多评分者融合策略，该策略能够有效利用评分者间的模糊性。其次，我们提出了一种基于模板的标签分布生成方法，该方法是针对TC估计任务量身定制的，并基于注释先验构建标签分布。第三，我们提出了一种新颖的受限分布损失，通过使用单峰损失和回归损失有效地正则化学习，显著提高了TC值估计。第四，据我们所知，我们是第一个同时利用评分者间和评分者内的变异性来解决乳腺肿瘤细胞密度估计任务中的标签模糊性问题的。在公开的BreastPathQ数据集上的实验结果表明，SLDL在很大程度上优于现有方法，并在TC估计任务中取得了新的最优结果。代码将可从https://github.com/PerceptionComputingLab/ULTRA获取。
