在本研究中，我们基于标签分布学习（LDL）范式，提出了一种新颖的框架来解决肿瘤细胞密度（TC）估计任务。我们提出了一种自组织标签分布学习框架（SLDL），以解决现有基于LDL的方法面临的挑战，包括难以探索不同评估者的模糊性、生成适当且灵活的标签分布以及准确恢复TC值。我们提出的SLDL在许多实验中进行了验证，并取得了显著的效果。首先，我们提出了一种专家感知条件变分自编码器，用于多样化的单评估者建模，以及一种基于注意力的多评估者融合策略，实现了有效的评估者模糊性探索。其次，我们提出了一种基于模板的标签分布生成方法，根据注释先验构建了适用于TC估计任务的标签分布。第三，我们提出了一种新颖的受限分布损失，通过有效地正则化学习，结合单峰损失和回归损失，大幅改善了TC值的估计效果。第四，据我们所知，我们是首个同时利用评估者间和评估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。对公开的BreastPathQ数据集进行的实验结果表明，SLDL在TC估计任务中的表现优于现有方法，并取得了新的最先进结果。代码可在https://github.com/PerceptionComputingLab/ULTRA获取。版权所有 © 2023 Elsevier B.V. 保留所有权利。

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.Copyright © 2023 Elsevier B.V. All rights reserved.