Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

IEEE Trans. Neural Netw. Learning Syst.

You

et al. 2024

Self Cite

Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPN) further addresses class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying finegrained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a finegrained subtype-aware alignment to improve performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype, while exhibiting disparate characteristics, because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers, and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multi-view congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtypeaware UDA, compared with state-of-the-art UDA methods.

Section: A Chd Transfer Taskmentioning

confidence: 99%

“…Also, -DR, -ω k , and -τ indicate subUDA without dimension reduction head, subtype balance weight, and semi-hard target mining, respectively. We note that the SubUDA was proposed in our prior work [24], which did not include the dynamic memory scheme.…”

Section: A Chd Transfer Taskmentioning

confidence: 99%

Subtype-Aware Dynamic Unsupervised Domain Adaptation

IEEE Trans. Neural Netw. Learning Syst.

You

et al. 2024

Self Cite

“…Unsupervised domain adaptation (UDA) has been proposed to address the problem of domain shift. 2 In UDA, a segmentation model is typically trained using both source and target data, but only the source data are labeled.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised domain adaptation for segmentation with black-box source model

Yoo

Medical Imaging 2022: Image Processing

et al. 2022

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Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of conventional solutions usually relies on the existence of both source and target domain data. However, privacy of the large-scale and well-labeled data in the source domain and trained model parameters can become the major concern of cross center/domain collaborations. In this work, to address this, we propose a practical solution to UDA for segmentation with a black-box segmentation model trained in the source domain only, rather than original source data or a white-box source model. Specifically, we resort to a knowledge distillation scheme with exponential mixup decay (EMD) to gradually learn target-specific representations. In addition, unsupervised entropy minimization is further applied to regularization of the target domain confidence. We evaluated our framework on the BraTS 2018 database, achieving performance on par with white-box source model adaptation approaches.

“…Besides, annotating data in the new target domain is costly and even infeasible [11]. To address this, unsupervised domain adaptation (UDA) was proposed to transfer knowledge from a labeled source domain to unlabeled target domains [13].…”

Section: Introductionmentioning

confidence: 99%

Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation

Lecture Notes in Computer Science

Yang

et al. 2021

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Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain, which is usually trained on data from both domains. Access to the source domain data at the adaptation stage, however, is often limited, due to data storage or privacy issues. To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an "off-the-shelf" segmentation model pre-trained in the source domain to the target domain, with an adaptive batch-wise normalization statistics adaptation framework. Specifically, the domain-specific low-order batch statistics, i.e., mean and variance, are gradually adapted with an exponential momentum decay scheme, while the consistency of domain shareable high-order batch statistics, i.e., scaling and shifting parameters, is explicitly enforced by our optimization objective. The transferability of each channel is adaptively measured first from which to balance the contribution of each channel. Moreover, the proposed source free UDA framework is orthogonal to unsupervised learning methods, e.g., self-entropy minimization, which can thus be simply added on top of our framework. Extensive experiments on the BraTS 2018 database show that our source free UDA framework outperformed existing source-relaxed UDA methods for the cross-subtype UDA segmentation task and yielded comparable results for the cross-modality UDA segmentation task, compared with a supervised UDA methods with the source data.