  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
