  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.
