Neoadjuvant therapy (NAT) for breast cancer is a common treatment option in clinical practice. Tumor cellularity (TC), which represents the percentage of invasive tumors in the tumor bed, has been widely used to quantify the response of breast cancer to NAT. Therefore, automatic TC estimation is significant in clinical practice. However, existing state-of-the-art methods usually take it as a TC score regression problem, which ignores the ambiguity of TC labels caused by subjective assessment or multiple raters. In this paper, to efficiently leverage the label ambiguities, we proposed an Uncertainty-aware Label disTRibution leArning (ULTRA) framework for automatic TC estimation. The proposed ULTRA first converted the single-value TC labels to discrete label distributions, which effectively models the ambiguity among all possible TC labels. Furthermore, the network learned TC label distributions by minimizing the Kullback-Leibler (KL) divergence between the predicted and ground-truth TC label distributions, which better supervised the model to leverage the ambiguity of TC labels. Moreover, the ULTRA mimicked the multi-rater fusion process in clinical practice with a multi-branch feature fusion module to further explore the uncertainties of TC labels. We evaluated the ULTRA on the public BreastPathQ dataset. The experimental results demonstrate that the ULTRA outperformed the regression-based methods for a large margin and achieved state-of-the-art results. The code will be available from https://github.com/PerceptionComputingLab/ULTRA
翻译:乳腺癌的Neoadjuvant疗法(NAT)是临床实践中常见的一种治疗方法。肿瘤细胞(TC)是肿瘤床内侵入性肿瘤的百分比,已被广泛用于量化乳腺癌对NAT的反应。因此,自动计算在临床实践中意义重大。然而,现有的最新技术方法通常将它视为TC的评分问题,忽视了主观评估或多重评分导致的TC标签的模糊性。在本文中,为了有效地利用标签的模糊性,我们提议为自动计算计算采用不确定性(TC)的肿瘤细胞细胞细胞(TC)框架。拟议的TELTRA首先将单一价值的TC标签转换为离散的标签分布,有效地模拟所有可能的TC标签之间的模糊性。此外,网络通过尽量减少Kullback-Lebel(K)的基数-Liber(L) 和地面图解结果(L)之间的差异性差值。 TC标签的分布将更好地监督模型,以便利用技术合作的模糊性(LAC) Q-RA (L) (T) (T) (T) (T) (T) (T) (T) (T) (T) (L) (L) (T) (L) (L) (L) (T) (L) (L) (T) (L) (O(L) (L) (L) (O(L) (T) (L) (L) (T) (L) (L) (T) (L) (的) (L) (L) (的) (L) (L) (L) (L) (L) (L) (L) (L) (T) (L) (L) (L) (L) (L) (T) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (L) (T) (T) (L) (L) (L) (L) (L) (T) (的) (L) (L) (L) (L) (L) ((T) (的) (的) (L)