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20202024
most citedAchieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies

3 citations · 3 across the 7 of their papers we have counts for

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eess.IV2022

Implicit U-Net for volumetric medical image segmentation

Sergio Naval Marimont, Giacomo Tarroni

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose th…

eess.IV2021

Implicit field learning for unsupervised anomaly detection in medical images

Sergio Naval Marimont, Giacomo Tarroni

We propose a novel unsupervised out-of-distribution detection method for medical images based on implicit fields image representations. In our approach, an auto-decoder feed-forwar…