ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Deep learning-based diagnostic models often suffer performance drops due to
distribution shifts between training (source) and test (target) domains.
Collecting and labeling sufficient target domain data for model retraining
represents an optimal solution, yet is limited by time and scarce resources.
Active learning (AL) offers an efficient approach to reduce annotation costs
while maintaining performance, but struggles to handle the challenge posed by
distribution variations across different datasets. In this study, we propose a
novel unsupervised Active learning framework for Domain Adaptation, named
ADAptation, which efficiently selects informative samples from multi-domain
data pools under limited annotation budget. As a fundamental step, our method
first utilizes the distribution homogenization capabilities of diffusion models
to bridge cross-dataset gaps by translating target images into source-domain
style. We then introduce two key innovations: (a) a hypersphere-constrained
contrastive learning network for compact feature clustering, and (b) a
dual-scoring mechanism that quantifies and balances sample uncertainty and
representativeness. Extensive experiments on four breast ultrasound datasets
(three public and one in-house/multi-center) across five common deep
classifiers demonstrate that our method surpasses existing strong AL-based
competitors, validating its effectiveness and generalization for clinical
domain adaptation. The code is available at the anonymized link:
https://github.com/miccai25-966/ADAptation.
