FLYER AI4HEALTH
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- A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
- Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approaches
- Reproducibility in machine learning for medical imaging
- Interpretability of Machine Learning Methods Applied to Neuroimaging
- Operationalizing the centiloid scale for [18 F]florbetapir PET studies on PET/MRI
- How can data augmentation improve attribution maps for disease subtype explainability?
- Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PET
- Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse
- Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data