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Unsupervised domain adaptation for the automated segmentation of neuroanatomy in MRI: a deep learning approach

https://doi.org/10.1101/845537
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/hbm.24803openalex-snapshot
Accurate and robust segmentation of neuroanatomy in T1‐weighted MRI by combining spatial priors with deep convolutional neural networks
inferredcites →https://doi.org/10.1002/jmri.21049openalex-snapshot
The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods
inferredcites →https://doi.org/10.1007/978-0-387-72076-0_18openalex-snapshot
Alzheimer’s Disease Neuroimaging Initiative
inferredcites →https://doi.org/10.1007/978-3-030-00928-1_60openalex-snapshot
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
inferredcites →https://doi.org/10.1007/978-3-030-01240-3_44openalex-snapshot
AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation
inferredcites →https://doi.org/10.1007/978-3-030-01240-3_50openalex-snapshot
Correction to: AugGAN: Cross Domain Adaptation with GAN-Based Data Augmentation
inferredcites →https://doi.org/10.1007/978-3-319-49409-8_35openalex-snapshot
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
inferredcites →https://doi.org/10.1007/978-3-319-58347-1_10openalex-snapshot
Domain-Adversarial Training of Neural Networks
inferredcites →https://doi.org/10.1007/978-3-319-59050-9_47openalex-snapshot
Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks
inferredcites →https://doi.org/10.1007/s12021-011-9133-yopenalex-snapshot
CANDIShare: A Resource for Pediatric Neuroimaging Data
inferredcites →https://doi.org/10.1016/j.neuroimage.2010.09.018openalex-snapshot
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.02.035openalex-snapshot
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
inferredcites →https://doi.org/10.1016/j.neuroimage.2017.04.039openalex-snapshot
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.03.025openalex-snapshot
A comparison of publicly available linear MRI stereotaxic registration techniques
inferredcites →https://doi.org/10.1016/j.neuroimage.2018.11.042openalex-snapshot
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
inferredcites →https://doi.org/10.1016/j.neuroimage.2019.03.026openalex-snapshot
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
inferredcites →https://doi.org/10.1016/j.nic.2005.09.008openalex-snapshot
The Alzheimer's Disease Neuroimaging Initiative
inferredcites →https://doi.org/10.1038/s41598-019-43299-zopenalex-snapshot
Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction
inferredcites →https://doi.org/10.1109/42.668698openalex-snapshot
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
inferredcites →https://doi.org/10.1109/cvpr.2016.265openalex-snapshot
Image Style Transfer Using Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.18openalex-snapshot
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.632openalex-snapshot
Image-to-Image Translation with Conditional Adversarial Networks
inferredcites →https://doi.org/10.1109/iccv.2017.244openalex-snapshot
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
inferredcites →https://doi.org/10.1109/tkde.2007.190672openalex-snapshot
Label Propagation through Linear Neighborhoods
inferredcites →https://doi.org/10.1109/tpami.2018.2858821openalex-snapshot
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
inferredcites →https://doi.org/10.1137/0330046openalex-snapshot
Acceleration of Stochastic Approximation by Averaging
inferredcites →https://doi.org/10.1145/1553374.1553411openalex-snapshot
Domain adaptation from multiple sources via auxiliary classifiers
inferredcites →https://doi.org/10.1162/jocn.2007.19.9.1498openalex-snapshot
Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults
inferredcites →https://doi.org/10.25080/majora-92bf1922-003openalex-snapshot
Theano: A CPU and GPU Math Compiler in Python
inferredcites →https://doi.org/10.3389/conf.neuro.11.2009.08.026openalex-snapshot
CANDIShare: A Resource for Pediatric Neuroimaging Data
inferredcites →https://doi.org/10.48550/arxiv.1411.1792openalex-snapshot
How transferable are features in deep neural networks?
inferredcites →https://doi.org/10.48550/arxiv.1412.3474openalex-snapshot
Deep Domain Confusion: Maximizing for Domain Invariance
inferredcites →https://doi.org/10.48550/arxiv.1502.02791openalex-snapshot
Learning Transferable Features with Deep Adaptation Networks
inferredcites →https://doi.org/10.48550/arxiv.1511.07289openalex-snapshot
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
inferredcites →https://doi.org/10.48550/arxiv.1603.04779openalex-snapshot
Revisiting Batch Normalization For Practical Domain Adaptation
inferredcites →https://doi.org/10.48550/arxiv.1610.02242openalex-snapshot
Temporal Ensembling for Semi-Supervised Learning
inferredcites →https://doi.org/10.48550/arxiv.1703.01780openalex-snapshot
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
inferredcites →https://doi.org/10.48550/arxiv.1706.05208openalex-snapshot
Self-ensembling for visual domain adaptation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s13042-024-02135-1openalex-snapshot
Unsupervised domain adaptation by incremental learning for concept drifting data streams
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.108729openalex-snapshot
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.104269openalex-snapshot
Domain adversarial networks and intensity-based data augmentation for male pelvic organ segmentation in cone beam CT
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109155openalex-snapshot
Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation
inferred← citeshttps://doi.org/10.1561/0600000095openalex-snapshot
Semantic Image Segmentation: Two Decades of Research
inferred← citeshttps://doi.org/10.3390/a18100636openalex-snapshot
Deep Learning for Brain MRI Tissue and Structure Segmentation: A Comprehensive Review
inferred← citeshttps://doi.org/10.70675/0043b404z3b9ez4e3fz9b71zce71f1ccc6e4openalex-snapshot
Identification de biomarqueurs par machine learning dans l'infarctus cérébral aigu
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