Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling
This addresses domain adaptation challenges in medical imaging where class proportions differ across institutions, offering a practical solution for real-world applications, though it is incremental as it builds on existing weakly-supervised methods.
The paper tackles the problem of domain shift in medical applications by proposing a weakly-supervised domain adaptation method that uses class proportion information from the target domain to assign pseudo-labels, improving performance without extra annotations. Experiments on endoscopic datasets show it outperforms semi-supervised techniques, even with only 5% labeled target data, and remains robust to noisy proportion labels.
Domain shift is a significant challenge in machine learning, particularly in medical applications where data distributions differ across institutions due to variations in data collection practices, equipment, and procedures. This can degrade performance when models trained on source domain data are applied to the target domain. Domain adaptation methods have been widely studied to address this issue, but most struggle when class proportions between the source and target domains differ. In this paper, we propose a weakly-supervised domain adaptation method that leverages class proportion information from the target domain, which is often accessible in medical datasets through prior knowledge or statistical reports. Our method assigns pseudo-labels to the unlabeled target data based on class proportion (called proportion-constrained pseudo-labeling), improving performance without the need for additional annotations. Experiments on two endoscopic datasets demonstrate that our method outperforms semi-supervised domain adaptation techniques, even when 5% of the target domain is labeled. Additionally, the experimental results with noisy proportion labels highlight the robustness of our method, further demonstrating its effectiveness in real-world application scenarios.