This study presents an unsupervised domain adaptation method aimed at
autonomously generating image masks outlining regions of interest (ROIs) for
differentiating breast lesions in breast ultrasound (US) imaging. Our
semi-supervised learning approach utilizes a primitive model trained on a small
public breast US dataset with true annotations. This model is then iteratively
refined for the domain adaptation task, generating pseudo-masks for our
private, unannotated breast US dataset. The dataset, twice the size of the
public one, exhibits considerable variability in image acquisition perspectives
and demographic representation, posing a domain-shift challenge. Unlike typical
domain adversarial training, we employ downstream classification outcomes as a
benchmark to guide the updating of pseudo-masks in subsequent iterations. We
found the classification precision to be highly correlated with the
completeness of the generated ROIs, which promotes the explainability of the
deep learning classification model. Preliminary findings demonstrate the
efficacy and reliability of this approach in streamlining the ROI annotation
process, thereby enhancing the classification and localization of breast
lesions for more precise and interpretable diagnoses.

本研究提出了一种无监督领域适应方法，用于自动生成乳腺超声成像中乳腺病变感兴趣区域（ROIs）的图像掩膜。我们的半监督学习方法利用在带有真实注释的小型公共乳腺超声数据集上训练的原始模型，针对领域适应任务进行迭代改进，生成私人未标记乳腺超声数据集的伪掩膜。该数据集的大小是公共数据集的两倍，并在图像采集角度和人群代表性方面具有相当的变异性，具有领域转移的挑战。与典型的领域对抗训练不同的是，我们使用下游分类结果作为基准，指导随后迭代中伪掩膜的更新。我们发现分类精度与生成的 ROIs 的完整性之间高度相关，促进了深度学习分类模型的解释性。初步研究结果证明了这种方法在简化 ROI 注释过程，从而增强乳腺病变的分类和定位以进行更精确和可解释的诊断方面的效果和可靠性。