ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2024
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- A curriculum-guided unified framework for robust unsupervised domain adaptation on multi-cohort Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Variance Extrapolated Class-Imbalance-Aware Domain Adaptive Myocardial Segmentation in Multi-Sequence Cardiac MRI.IEEE journal of biomedical and health informatics · 2026Article
- NeuroLangSeg: Language-Guided Subcortical Segmentation with Pseudo-Supervision and Anatomical-Linguistic Validation.Proceedings of machine learning research · 2026Article
- From Unsupervised to Zero-Shot: 3D Domain Adaptation for Cryo-Electron Tomography Segmentation.International journal of computer vision · 2026Article
- Perinatal hardship and infant neurodevelopment: insights from a global pandemic.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI.Perinatal, preterm and paediatric image analysis (2025) · 2026Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a
Identifiers
What OpenQuestion holds
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.