  We focus on the problem of Unsupervised Domain Adaptation (\uda) for breast
cancer detection from mammograms (BCDM) problem. Recent advancements have shown
that masked image modeling serves as a robust pretext task for UDA. However,
when applied to cross-domain BCDM, these techniques struggle with breast
abnormalities such as masses, asymmetries, and micro-calcifications, in part
due to the typically much smaller size of region of interest in comparison to
natural images. This often results in more false positives per image (FPI) and
significant noise in pseudo-labels typically used to bootstrap such techniques.
Recognizing these challenges, we introduce a transformer-based Domain-invariant
Mask Annealed Student Teacher autoencoder (D-MASTER) framework. D-MASTER
adaptively masks and reconstructs multi-scale feature maps, enhancing the
model's ability to capture reliable target domain features. D-MASTER also
includes adaptive confidence refinement to filter pseudo-labels, ensuring only
high-quality detections are considered. We also provide a bounding box
annotated subset of 1000 mammograms from the RSNA Breast Screening Dataset
(referred to as RSNA-BSD1K) to support further research in BCDM. We evaluate
D-MASTER on multiple BCDM datasets acquired from diverse domains. Experimental
results show a significant improvement of 9% and 13% in sensitivity at 0.3 FPI
over state-of-the-art UDA techniques on publicly available benchmark INBreast
and DDSM datasets respectively. We also report an improvement of 11% and 17% on
In-house and RSNA-BSD1K datasets respectively. The source code, pre-trained
D-MASTER model, along with RSNA-BSD1K dataset annotations is available at
https://dmaster-iitd.github.io/webpage.
