Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation

Abstract

Unsupervised domain adaptation (UDA) is an emerging technique that enables the transfer of domain knowledge learned from a labeled source domain to unlabeled target domains, providing a way of coping with the difficulty of labeling in new domains. The majority of prior work has relied on both source and target domain data for adaptation. However, because of privacy concerns about potential leaks in sensitive information contained in patient data, it is often challenging to share the data and labels in the source domain and trained model parameters in cross-center collaborations. To address this issue, we propose a practical framework for UDA with a black-box segmentation model trained in the source domain only, without relying on source data or a white-box source model in which the network parameters are accessible. In particular, we propose a knowledge distillation scheme to gradually learn target-specific representations. Additionally, we regularize the confidence of the labels in the target domain via unsupervised entropy minimization, leading to performance gain over UDA without entropy minimization. We extensively validated our framework on a few datasets and deep learning backbones, demonstrating the potential for our framework to be applied in challenging yet realistic clinical settings.

Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

“…To address this, a recent work (Liu et al, 2022h) uses black-box UDA segmentation, for which no prior knowledge of network weights is needed for adaptation. Liu et al (2022i) further propose that a target domain network structure could be different from a trained source domain model to achieve UDA for segmentation.…”

ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training

“…While recently flourished deep learning methods excel at segmenting those structures, deep learning-based segmentors cannot generalize well in a heterogeneous domain, e.g., different clinical centers, scanner vendors, or imaging modalities [20,16,14,4]. To alleviate this issue, unsupervised domain adaptation (UDA) has been actively developed, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain [5,15,18,19]. Due to diverse target domains, however, the performance of UDA is far from satisfactory [31,9,17].…”