MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammograms
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Accurate lesion tracking in temporal mammograms is essential for monitoring
breast cancer progression and facilitating early diagnosis. However, automated
lesion correspondence across exams remains a challenges in computer-aided
diagnosis (CAD) systems, limiting their effectiveness. We propose MammoTracker,
a mask-guided lesion tracking framework that automates lesion localization
across consecutively exams. Our approach follows a coarse-to-fine strategy
incorporating three key modules: global search, local search, and score
refinement. To support large-scale training and evaluation, we introduce a new
dataset with curated prior-exam annotations for 730 mass and calcification
cases from the public EMBED mammogram dataset, yielding over 20000 lesion
pairs, making it the largest known resource for temporal lesion tracking in
mammograms. Experimental results demonstrate that MammoTracker achieves 0.455
average overlap and 0.509 accuracy, surpassing baseline models by 8%,
highlighting its potential to enhance CAD-based lesion progression analysis.
Our dataset will be available at
https://gitlab.oit.duke.edu/railabs/LoGroup/mammotracker.
