ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2021
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Source-free unsupervised domain adaptation: A survey.Neural networks : the official journal of the International Neural Network Society · 2024Pooled it
- VP-SFDA: Visual Prompt Source-Free Domain Adaptation for Cross-Modal Medical Image.Health data science · 2025Article
- 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
- Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.Medical image analysis · 2023Article
- Article
- Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.ArXiv · 2023Article
- Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning.Frontiers in neuroscience · 2023Article
- Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation.Medical image analysis · 2023Article
- Dual adversarial models with cross-coordination consistency constraint for domain adaption in brain tumor segmentation.Frontiers in neuroscience · 2023Article
- Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging.Physics in medicine and biology · 2022Article
- Brain MR Atlas Construction Using Symmetric Deep Neural Inpainting.IEEE journal of biomedical and health informatics · 2022Article
- SELF-SEMANTIC CONTOUR ADAPTATION FOR CROSS MODALITY BRAIN TUMOR SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation.Frontiers in neuroscience · 2022Article
- Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2021Article
- Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.