ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2021
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis.
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 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Attentive continuous generative self-training for unsupervised domain adaptive medical image translation.Medical image analysis · 2023Article
- Transforming Complex Problems Into K-Means Solutions.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.ArXiv · 2023Article
- Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation.Medical image analysis · 2023Article
- ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-Training.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 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
- Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2021Article
- Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement.Proceedings of SPIE--the International Society for Optical EngineeringArticle
- Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
8 authors.
Funding
Abstract
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-training UDA for generative tasks, such as image synthesis, is not fully investigated. In this work, we propose a novel generative self-training (GST) UDA framework with continuous value prediction and regression objective for cross-domain image synthesis. Specifically, we propose to filter the pseudo-label with an uncertainty mask, and quantify the predictive confidence of generated images with practical variational Bayes learning. The fast test-time adaptation is achieved by a round-based alternative optimization scheme. We validated our framework on the tagged-to-cine magnetic resonance imaging (MRI) synthesis problem, where datasets in the source and target domains were acquired from different scanners or centers. Extensive validations were carried out to verify our framework against popular adversarial training UDA methods. Results show that our GST, with tagged MRI of test subjects in new target domains, improved the synthesis quality by a large margin, compared with the adversarial training UDA methods.
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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.