ArticleMedical image analysis2023
CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea 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 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- The impact of artificial intelligence in the diagnosis and management of acoustic neuroma: A systematic review.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- DARE-FUSE: domain aligned evidence guided learning for joint brain tumor MRI segmentation and classification.NPJ digital medicine · 2026Article
- Unified Cross-Modal Medical Image Synthesis With Hierarchical Mixture of Product-of-Experts.IEEE transactions on pattern analysis and machine intelligence · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Large-vocabulary segmentation for medical images with text prompts.NPJ digital medicine · 2025Article
- Article
- Optimal Res-UNET architecture with deep supervision for tumor segmentation.Frontiers in medicine · 2025Article
- Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool.Scientific reports · 2024Article
- Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation.Scientific reports · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.Frontiers in computational neuroscience · 2024Article
- Article
- Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations.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
- Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation.ArXiv · 2023Article
- CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.Medical image analysis · 2023Article
Corrections and comments
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Authors and funding
40 authors.
Funding
Abstract
Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT
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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.