ArticleMedical image analysis2023
Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Variance Extrapolated Class-Imbalance-Aware Domain Adaptive Myocardial Segmentation in Multi-Sequence Cardiac MRI.IEEE journal of biomedical and health informatics · 2026Article
- Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2025Article
- Evaluating segment anything model (SAM) on MRI scans of brain tumors.Scientific reports · 2024Article
- EXPLORING BACKDOOR ATTACKS IN OFF-THE-SHELF UNSUPERVISED DOMAIN ADAPTATION FOR SECURING CARDIAC MRI-BASED DIAGNOSIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Source-free domain transfer algorithm with reduced style sensitivity for medical image segmentation.PloS one · 2024Article
- Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Self-supervised Semantic Segmentation: Consistency over Transformation.... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision · 2023Article
- Article
- SUCCESSIVE SUBSPACE LEARNING FOR CARDIAC DISEASE CLASSIFICATION WITH TWO-PHASE DEFORMATION FIELDS FROM CINE MRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
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4 authors.
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Abstract
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is typically jointly trained on data from both domains, accessing the labeled source domain data is often restricted, due to concerns over patient data privacy or intellectual property. To sidestep this, we propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation. Toward this goal, we aim to develop a novel batch-wise normalization (BN) statistics adaptation framework. In particular, we gradually adapt the domain-specific low-order BN statistics, e.g., mean and variance, through an exponential momentum decay strategy, while explicitly enforcing the consistency of the domain shareable high-order BN statistics, e.g., scaling and shifting factors, via our optimization objective. We also adaptively quantify the channel-wise transferability to gauge the importance of each channel, via both low-order statistics divergence and a scaling factor. Furthermore, we incorporate unsupervised self-entropy minimization into our framework to boost performance alongside a novel queued, memory-consistent self-training strategy to utilize the reliable pseudo label for stable and efficient unsupervised adaptation. We evaluated our OSUDA-based framework on both cross-modality and cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks. Our experimental results showed that our memory consistent OSUDA performs better than existing source-relaxed UDA methods and yields similar performance to UDA methods with source data.
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