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20232026
most citedEvaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET

8 citations · 16 across the 7 of their papers we have counts for

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eess.IV2023★ 3 cited

Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET

Maëlys Solal, Ravi Hassanaly, Ninon Burgos

Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on b…

eess.IV2023★ 3 cited

A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation

Francesco Galati, Daniele Falcetta, Rosa Cortese +4

We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality…