Birgit Ertl-Wagner
Orcid: 0000-0002-7896-7049Affiliations:
- University of Toronto, Canada
According to our database1,
Birgit Ertl-Wagner
authored at least 15 papers
between 2017 and 2024.
Collaborative distances:
Collaborative distances:
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Online presence:
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on sickkids.ca
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Bibliography
2024
Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks.
CoRR, 2024
2023
Motion artifact correction in fetal MRI based on a Generative Adversarial network method.
Biomed. Signal Process. Control., March, 2023
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks.
CoRR, 2023
2022
Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI.
Frontiers Artif. Intell., 2022
A novel GAN-based paradigm for weakly supervised brain tumor segmentation of MR images.
CoRR, 2022
Tumor-location-guided CNNs for Pediatric Low-grade Glioma Molecular Biomarker Classification Using MRI.
CoRR, 2022
Open-radiomics: A Research Protocol to Make Radiomics-based Machine Learning Pipelines Reproducible.
CoRR, 2022
Improving the Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
2021
Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.
Sensors, 2021
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning.
CoRR, 2021
2020
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease.
CoRR, 2020
2018
Handedness-dependent functional organizational patterns within the bilateral vestibular cortical network revealed by fMRI connectivity based parcellation.
NeuroImage, 2018
2017
Test-retest reliability of prefrontal transcranial Direct Current Stimulation (tDCS) effects on functional MRI connectivity in healthy subjects.
NeuroImage, 2017
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound.
Comput. Vis. Image Underst., 2017